746 lines
25 KiB
HTML
746 lines
25 KiB
HTML
<!DOCTYPE html>
|
||
<html lang="zh-CN">
|
||
<head>
|
||
<meta charset="UTF-8">
|
||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||
<title>TRELLIS.2 - 微软开源 Image-to-3D 生成模型</title>
|
||
<style>
|
||
* { margin: 0; padding: 0; box-sizing: border-box; }
|
||
body {
|
||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
|
||
background: #050508; color: #e0e0e0;
|
||
min-height: 100vh;
|
||
}
|
||
|
||
/* Hero */
|
||
.hero {
|
||
position: relative;
|
||
padding: 5rem 2rem 4rem;
|
||
text-align: center;
|
||
background: radial-gradient(ellipse at 50% 0%, rgba(99,102,241,0.15) 0%, transparent 70%);
|
||
border-bottom: 1px solid #1a1a2e;
|
||
}
|
||
.badge {
|
||
display: inline-block;
|
||
padding: 0.35rem 1rem;
|
||
background: rgba(99,102,241,0.15);
|
||
border: 1px solid rgba(99,102,241,0.3);
|
||
border-radius: 20px;
|
||
font-size: 0.8rem;
|
||
color: #818cf8;
|
||
margin-bottom: 1.5rem;
|
||
letter-spacing: 0.5px;
|
||
}
|
||
.hero h1 {
|
||
font-size: 3.2rem; font-weight: 800;
|
||
background: linear-gradient(135deg, #818cf8 0%, #c084fc 50%, #f472b6 100%);
|
||
-webkit-background-clip: text; -webkit-text-fill-color: transparent;
|
||
background-clip: text;
|
||
margin-bottom: 1rem;
|
||
line-height: 1.2;
|
||
}
|
||
.hero .tagline {
|
||
font-size: 1.25rem; color: #888; max-width: 700px;
|
||
margin: 0 auto 2.5rem; line-height: 1.6;
|
||
}
|
||
.hero-stats {
|
||
display: flex; gap: 2rem; justify-content: center; flex-wrap: wrap;
|
||
}
|
||
.stat {
|
||
text-align: center;
|
||
}
|
||
.stat-value {
|
||
font-size: 2rem; font-weight: 700; color: #c084fc;
|
||
display: block;
|
||
}
|
||
.stat-label {
|
||
font-size: 0.8rem; color: #666; text-transform: uppercase;
|
||
letter-spacing: 1px; margin-top: 0.25rem;
|
||
}
|
||
|
||
/* Container */
|
||
.container { max-width: 1100px; margin: 0 auto; padding: 0 2rem; }
|
||
|
||
/* Section */
|
||
.section { padding: 4rem 0; }
|
||
.section-title {
|
||
font-size: 1.8rem; font-weight: 700; color: #e0e0e0;
|
||
margin-bottom: 0.5rem;
|
||
}
|
||
.section-desc {
|
||
color: #666; margin-bottom: 2.5rem; font-size: 1rem;
|
||
}
|
||
|
||
/* Cards Grid */
|
||
.grid-2 { display: grid; grid-template-columns: 1fr 1fr; gap: 1.5rem; }
|
||
.grid-3 { display: grid; grid-template-columns: repeat(3, 1fr); gap: 1.5rem; }
|
||
@media (max-width: 768px) {
|
||
.grid-2, .grid-3 { grid-template-columns: 1fr; }
|
||
.hero h1 { font-size: 2rem; }
|
||
}
|
||
|
||
.card {
|
||
background: #0c0c14;
|
||
border: 1px solid #1a1a2e;
|
||
border-radius: 16px;
|
||
padding: 2rem;
|
||
transition: border-color 0.3s, transform 0.3s;
|
||
}
|
||
.card:hover {
|
||
border-color: #333;
|
||
transform: translateY(-2px);
|
||
}
|
||
.card-icon {
|
||
width: 48px; height: 48px;
|
||
background: rgba(99,102,241,0.1);
|
||
border-radius: 12px;
|
||
display: flex; align-items: center; justify-content: center;
|
||
font-size: 1.5rem;
|
||
margin-bottom: 1.2rem;
|
||
}
|
||
.card h3 {
|
||
font-size: 1.15rem; color: #e0e0e0;
|
||
margin-bottom: 0.75rem; font-weight: 600;
|
||
}
|
||
.card p {
|
||
color: #888; line-height: 1.7; font-size: 0.95rem;
|
||
}
|
||
|
||
/* Feature highlight card */
|
||
.card-highlight {
|
||
background: linear-gradient(135deg, rgba(99,102,241,0.08), rgba(192,132,252,0.05));
|
||
border-color: rgba(99,102,241,0.2);
|
||
}
|
||
|
||
/* Speed benchmark */
|
||
.benchmark { margin-top: 1rem; }
|
||
.bench-row {
|
||
display: flex; align-items: center; gap: 1rem;
|
||
margin-bottom: 0.75rem;
|
||
}
|
||
.bench-label {
|
||
font-size: 0.85rem; color: #aaa; width: 80px; flex-shrink: 0;
|
||
text-align: right;
|
||
}
|
||
.bench-bar-bg {
|
||
flex: 1; height: 28px; background: #111; border-radius: 6px;
|
||
overflow: hidden; position: relative;
|
||
}
|
||
.bench-bar {
|
||
height: 100%; border-radius: 6px;
|
||
display: flex; align-items: center; padding-left: 12px;
|
||
font-size: 0.8rem; font-weight: 600; color: #fff;
|
||
transition: width 1s ease;
|
||
}
|
||
.bench-bar.s512 {
|
||
width: 5%; min-width: 60px;
|
||
background: linear-gradient(90deg, #818cf8, #6366f1);
|
||
}
|
||
.bench-bar.s1024 {
|
||
width: 28%;
|
||
background: linear-gradient(90deg, #a78bfa, #7c3aed);
|
||
}
|
||
.bench-bar.s1536 {
|
||
width: 100%;
|
||
background: linear-gradient(90deg, #c084fc, #9333ea);
|
||
}
|
||
|
||
/* PBR attributes */
|
||
.pbr-grid {
|
||
display: grid; grid-template-columns: repeat(4, 1fr); gap: 1rem;
|
||
margin-top: 1rem;
|
||
}
|
||
.pbr-item {
|
||
text-align: center; padding: 1.2rem 0.5rem;
|
||
background: #0a0a12;
|
||
border: 1px solid #1a1a2e;
|
||
border-radius: 12px;
|
||
}
|
||
.pbr-circle {
|
||
width: 48px; height: 48px; border-radius: 50%;
|
||
margin: 0 auto 0.75rem;
|
||
border: 2px solid #333;
|
||
}
|
||
.pbr-item span {
|
||
font-size: 0.8rem; color: #999;
|
||
}
|
||
@media (max-width: 600px) {
|
||
.pbr-grid { grid-template-columns: 1fr 1fr; }
|
||
}
|
||
|
||
/* Pipeline */
|
||
.pipeline {
|
||
display: flex; align-items: center; gap: 0;
|
||
flex-wrap: wrap; justify-content: center;
|
||
margin-top: 1rem;
|
||
}
|
||
.pipe-step {
|
||
background: #0c0c14;
|
||
border: 1px solid #1a1a2e;
|
||
border-radius: 12px;
|
||
padding: 1.5rem 1.2rem;
|
||
text-align: center;
|
||
flex: 1; min-width: 140px;
|
||
}
|
||
.pipe-step .icon { font-size: 2rem; margin-bottom: 0.5rem; }
|
||
.pipe-step h4 { color: #c084fc; font-size: 0.9rem; margin-bottom: 0.3rem; }
|
||
.pipe-step p { color: #666; font-size: 0.75rem; }
|
||
.pipe-arrow {
|
||
font-size: 1.5rem; color: #333; padding: 0 0.5rem;
|
||
}
|
||
@media (max-width: 600px) {
|
||
.pipe-arrow { transform: rotate(90deg); }
|
||
}
|
||
|
||
/* Output format tags */
|
||
.format-tags {
|
||
display: flex; gap: 0.75rem; flex-wrap: wrap; margin-top: 1rem;
|
||
}
|
||
.format-tag {
|
||
padding: 0.5rem 1.2rem;
|
||
background: rgba(99,102,241,0.08);
|
||
border: 1px solid rgba(99,102,241,0.2);
|
||
border-radius: 8px;
|
||
font-size: 0.9rem; color: #818cf8;
|
||
font-weight: 600;
|
||
}
|
||
|
||
/* Use cases */
|
||
.usecase-card {
|
||
display: flex; align-items: flex-start; gap: 1rem;
|
||
padding: 1.5rem;
|
||
}
|
||
.usecase-num {
|
||
font-size: 2rem; font-weight: 800;
|
||
color: rgba(99,102,241,0.3);
|
||
line-height: 1;
|
||
flex-shrink: 0;
|
||
}
|
||
.usecase-card h3 { margin-bottom: 0.5rem; }
|
||
|
||
/* Requirements table */
|
||
.req-table {
|
||
width: 100%;
|
||
border-collapse: collapse;
|
||
margin-top: 1rem;
|
||
}
|
||
.req-table th, .req-table td {
|
||
padding: 1rem 1.2rem;
|
||
text-align: left;
|
||
border-bottom: 1px solid #1a1a2e;
|
||
}
|
||
.req-table th {
|
||
color: #888; font-size: 0.8rem; text-transform: uppercase;
|
||
letter-spacing: 1px; font-weight: 500;
|
||
}
|
||
.req-table td { color: #ccc; font-size: 0.95rem; }
|
||
.req-table tr:last-child td { border-bottom: none; }
|
||
.req-val { color: #c084fc; font-weight: 600; }
|
||
|
||
/* Deploy options */
|
||
.deploy-option {
|
||
display: flex; align-items: flex-start; gap: 1.2rem;
|
||
padding: 1.5rem 0;
|
||
border-bottom: 1px solid #1a1a2e;
|
||
}
|
||
.deploy-option:last-child { border-bottom: none; }
|
||
.deploy-icon {
|
||
width: 44px; height: 44px;
|
||
background: rgba(99,102,241,0.1);
|
||
border-radius: 10px;
|
||
display: flex; align-items: center; justify-content: center;
|
||
font-size: 1.3rem; flex-shrink: 0;
|
||
}
|
||
.deploy-option h4 {
|
||
color: #e0e0e0; font-size: 1rem; margin-bottom: 0.3rem;
|
||
}
|
||
.deploy-option p { color: #888; font-size: 0.9rem; line-height: 1.6; }
|
||
.deploy-cost {
|
||
display: inline-block; padding: 0.2rem 0.6rem;
|
||
background: rgba(52,211,153,0.1);
|
||
border: 1px solid rgba(52,211,153,0.2);
|
||
border-radius: 6px;
|
||
font-size: 0.75rem; color: #34d399;
|
||
margin-top: 0.3rem;
|
||
}
|
||
|
||
/* Code block */
|
||
.code-block {
|
||
background: #0a0a12;
|
||
border: 1px solid #1a1a2e;
|
||
border-radius: 10px;
|
||
padding: 1.5rem;
|
||
font-family: "SF Mono", "Fira Code", monospace;
|
||
font-size: 0.85rem;
|
||
color: #a5b4fc;
|
||
overflow-x: auto;
|
||
margin-top: 1rem;
|
||
line-height: 1.8;
|
||
}
|
||
.code-comment { color: #4a5568; }
|
||
.code-cmd { color: #c084fc; }
|
||
|
||
/* Links bar */
|
||
.links {
|
||
display: flex; gap: 1rem; flex-wrap: wrap;
|
||
padding: 3rem 0; justify-content: center;
|
||
}
|
||
.link-btn {
|
||
display: inline-flex; align-items: center; gap: 0.5rem;
|
||
padding: 0.75rem 1.5rem;
|
||
background: rgba(99,102,241,0.1);
|
||
border: 1px solid rgba(99,102,241,0.25);
|
||
border-radius: 10px;
|
||
color: #818cf8;
|
||
text-decoration: none;
|
||
font-size: 0.95rem;
|
||
font-weight: 500;
|
||
transition: all 0.3s;
|
||
}
|
||
.link-btn:hover {
|
||
background: rgba(99,102,241,0.2);
|
||
border-color: rgba(99,102,241,0.5);
|
||
transform: translateY(-1px);
|
||
}
|
||
.link-btn.primary {
|
||
background: linear-gradient(135deg, #6366f1, #7c3aed);
|
||
border-color: transparent;
|
||
color: #fff;
|
||
}
|
||
|
||
/* Status banner */
|
||
.status-banner {
|
||
background: linear-gradient(90deg, rgba(251,191,36,0.08), rgba(251,191,36,0.02));
|
||
border: 1px solid rgba(251,191,36,0.2);
|
||
border-radius: 12px;
|
||
padding: 1.2rem 1.5rem;
|
||
display: flex; align-items: center; gap: 1rem;
|
||
margin-bottom: 3rem;
|
||
}
|
||
.status-dot {
|
||
width: 10px; height: 10px; border-radius: 50%;
|
||
background: #fbbf24;
|
||
animation: pulse 2s infinite;
|
||
flex-shrink: 0;
|
||
}
|
||
@keyframes pulse {
|
||
0%, 100% { opacity: 1; }
|
||
50% { opacity: 0.4; }
|
||
}
|
||
.status-banner p { color: #d4a017; font-size: 0.9rem; }
|
||
|
||
/* Divider */
|
||
.divider {
|
||
height: 1px;
|
||
background: linear-gradient(90deg, transparent, #1a1a2e, transparent);
|
||
}
|
||
|
||
/* Footer */
|
||
footer {
|
||
text-align: center;
|
||
padding: 3rem 2rem;
|
||
color: #444;
|
||
font-size: 0.85rem;
|
||
border-top: 1px solid #111;
|
||
}
|
||
footer a { color: #666; text-decoration: none; }
|
||
</style>
|
||
</head>
|
||
<body>
|
||
|
||
<!-- Hero -->
|
||
<section class="hero">
|
||
<div class="badge">MIT License · Microsoft Research</div>
|
||
<h1>TRELLIS.2</h1>
|
||
<p class="tagline">
|
||
一张图片，生成高保真 3D 模型。<br>
|
||
4B 参数，支持 PBR 材质、任意拓扑，开源可商用。
|
||
</p>
|
||
<div class="hero-stats">
|
||
<div class="stat">
|
||
<span class="stat-value">4B</span>
|
||
<span class="stat-label">模型参数</span>
|
||
</div>
|
||
<div class="stat">
|
||
<span class="stat-value">1536³</span>
|
||
<span class="stat-label">最高分辨率</span>
|
||
</div>
|
||
<div class="stat">
|
||
<span class="stat-value">~3s</span>
|
||
<span class="stat-label">512³ 生成速度</span>
|
||
</div>
|
||
<div class="stat">
|
||
<span class="stat-value">MIT</span>
|
||
<span class="stat-label">开源协议</span>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="container">
|
||
|
||
<!-- Status -->
|
||
<div style="padding-top: 3rem;">
|
||
<div class="status-banner">
|
||
<div class="status-dot"></div>
|
||
<p>源码已拉取，等待 GPU 到位后部署。当前状态：待部署</p>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- What is TRELLIS.2 -->
|
||
<section class="section">
|
||
<h2 class="section-title">这是什么</h2>
|
||
<p class="section-desc">微软研究院开源的 Image-to-3D 生成模型，一张照片即可生成带 PBR 材质的生产级 3D 资产</p>
|
||
|
||
<div class="grid-2">
|
||
<div class="card card-highlight">
|
||
<div class="card-icon">📷</div>
|
||
<h3>图片转 3D 模型</h3>
|
||
<p>上传 1~4 张产品照片（正面必须，左/右/背面可选），AI 自动生成完整的 3D 模型。支持任意物体：产品、角色、建筑、家具、食物等。</p>
|
||
</div>
|
||
<div class="card card-highlight">
|
||
<div class="card-icon">🎨</div>
|
||
<h3>PBR 物理材质</h3>
|
||
<p>生成的模型自带基础色、金属度、粗糙度、透明度四种 PBR 属性，可直接在任何 3D 引擎中使用，支持环境光照下的真实感重光照。</p>
|
||
</div>
|
||
<div class="card">
|
||
<div class="card-icon">🧩</div>
|
||
<h3>任意拓扑结构</h3>
|
||
<p>不同于传统方法只能处理封闭表面，TRELLIS.2 可以处理开放表面、非流形几何、内部封闭结构等复杂拓扑，保留锐利边缘和精细细节。</p>
|
||
</div>
|
||
<div class="card">
|
||
<div class="card-icon">⚡</div>
|
||
<h3>极速生成</h3>
|
||
<p>基于 O-Voxel 稀疏体素表示和 Flow Matching Transformer，512³ 分辨率仅需 3 秒，1024³ 仅需 17 秒（H100 GPU）。</p>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Generation Pipeline -->
|
||
<section class="section">
|
||
<h2 class="section-title">生成流程</h2>
|
||
<p class="section-desc">三阶段流水线：从图片到可用的 3D 资产</p>
|
||
|
||
<div class="pipeline">
|
||
<div class="pipe-step">
|
||
<div class="icon">🖼️</div>
|
||
<h4>输入图片</h4>
|
||
<p>1~4 个视角的产品照片</p>
|
||
</div>
|
||
<span class="pipe-arrow">→</span>
|
||
<div class="pipe-step">
|
||
<div class="icon">🧊</div>
|
||
<h4>稀疏结构</h4>
|
||
<p>生成 O-Voxel 稀疏体素</p>
|
||
</div>
|
||
<span class="pipe-arrow">→</span>
|
||
<div class="pipe-step">
|
||
<div class="icon">📐</div>
|
||
<h4>形状生成</h4>
|
||
<p>重建几何细节与拓扑</p>
|
||
</div>
|
||
<span class="pipe-arrow">→</span>
|
||
<div class="pipe-step">
|
||
<div class="icon">✨</div>
|
||
<h4>材质贴图</h4>
|
||
<p>PBR 纹理 + 颜色映射</p>
|
||
</div>
|
||
<span class="pipe-arrow">→</span>
|
||
<div class="pipe-step">
|
||
<div class="icon">📦</div>
|
||
<h4>导出</h4>
|
||
<p>GLB / OBJ / FBX / STL</p>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Speed Benchmark -->
|
||
<section class="section">
|
||
<h2 class="section-title">生成速度</h2>
|
||
<p class="section-desc">在 NVIDIA H100 GPU 上的基准测试</p>
|
||
|
||
<div class="card">
|
||
<div class="benchmark">
|
||
<div class="bench-row">
|
||
<span class="bench-label">512³</span>
|
||
<div class="bench-bar-bg">
|
||
<div class="bench-bar s512">~3s</div>
|
||
</div>
|
||
</div>
|
||
<div class="bench-row">
|
||
<span class="bench-label">1024³</span>
|
||
<div class="bench-bar-bg">
|
||
<div class="bench-bar s1024">~17s (10s 形状 + 7s 材质)</div>
|
||
</div>
|
||
</div>
|
||
<div class="bench-row">
|
||
<span class="bench-label">1536³</span>
|
||
<div class="bench-bar-bg">
|
||
<div class="bench-bar s1536">~60s (35s 形状 + 25s 材质)</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
<p style="color:#555; font-size:0.8rem; margin-top:1rem;">
|
||
* O-Voxel 到 Mesh 的转换额外耗时 <100ms（CUDA）或 <10s（CPU）
|
||
</p>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- PBR Materials -->
|
||
<section class="section">
|
||
<h2 class="section-title">PBR 材质属性</h2>
|
||
<p class="section-desc">生成的每个 3D 模型都包含完整的物理材质信息，支持真实感渲染与重光照</p>
|
||
|
||
<div class="pbr-grid">
|
||
<div class="pbr-item">
|
||
<div class="pbr-circle" style="background: linear-gradient(135deg, #e74c3c, #3498db, #2ecc71);"></div>
|
||
<span>Base Color<br>基础色</span>
|
||
</div>
|
||
<div class="pbr-item">
|
||
<div class="pbr-circle" style="background: linear-gradient(135deg, #bdc3c7, #ecf0f1); border-color: #555;"></div>
|
||
<span>Metallic<br>金属度</span>
|
||
</div>
|
||
<div class="pbr-item">
|
||
<div class="pbr-circle" style="background: linear-gradient(135deg, #2c3e50, #7f8c8d);"></div>
|
||
<span>Roughness<br>粗糙度</span>
|
||
</div>
|
||
<div class="pbr-item">
|
||
<div class="pbr-circle" style="background: linear-gradient(135deg, rgba(255,255,255,0.8), rgba(255,255,255,0.1)); border-color: #444;"></div>
|
||
<span>Opacity<br>透明度</span>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Output Formats -->
|
||
<section class="section">
|
||
<h2 class="section-title">导出格式</h2>
|
||
<p class="section-desc">生成的 3D 资产可导出为多种业界标准格式，直接用于游戏引擎、3D 软件、电商平台</p>
|
||
|
||
<div class="format-tags">
|
||
<span class="format-tag">GLB / glTF</span>
|
||
<span class="format-tag">OBJ</span>
|
||
<span class="format-tag">FBX</span>
|
||
<span class="format-tag">STL</span>
|
||
<span class="format-tag">MP4 预览视频</span>
|
||
</div>
|
||
|
||
<div class="grid-3" style="margin-top: 1.5rem;">
|
||
<div class="card">
|
||
<h3>GLB / glTF</h3>
|
||
<p>Web 3D 首选格式，内嵌 PBR 纹理，可直接用于网页 3D 查看器、AR 预览、电商平台商品展示。</p>
|
||
</div>
|
||
<div class="card">
|
||
<h3>OBJ / FBX</h3>
|
||
<p>传统 3D 工作流格式，兼容 Blender、Maya、3ds Max、Cinema 4D 等所有主流 3D 软件。</p>
|
||
</div>
|
||
<div class="card">
|
||
<h3>STL</h3>
|
||
<p>3D 打印专用格式，生成的模型可直接送去打印实体原型。</p>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Use Cases -->
|
||
<section class="section">
|
||
<h2 class="section-title">应用场景</h2>
|
||
<p class="section-desc">从电商到游戏，覆盖多种 3D 内容需求</p>
|
||
|
||
<div class="grid-2">
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">01</span>
|
||
<div>
|
||
<h3>电商产品 3D 展示</h3>
|
||
<p>上传产品照片，生成可 360° 旋转的 3D 模型，嵌入商品详情页。告别传统多角度拍摄，一张图搞定全部角度。</p>
|
||
</div>
|
||
</div>
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">02</span>
|
||
<div>
|
||
<h3>品牌视觉 & 广告</h3>
|
||
<p>生成带真实材质的 3D 产品模型，放入任意虚拟场景中，自由调整光照和角度，快速产出品牌视觉素材。</p>
|
||
</div>
|
||
</div>
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">03</span>
|
||
<div>
|
||
<h3>游戏 & 虚拟场景</h3>
|
||
<p>从概念图或参考照片快速生成游戏道具、场景物件。PBR 材质可直接导入 Unity / Unreal Engine 使用。</p>
|
||
</div>
|
||
</div>
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">04</span>
|
||
<div>
|
||
<h3>3D 打印原型</h3>
|
||
<p>产品设计初期，用照片快速生成 3D 模型并导出 STL，直接送去 3D 打印验证外观和尺寸。</p>
|
||
</div>
|
||
</div>
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">05</span>
|
||
<div>
|
||
<h3>AR / VR 内容</h3>
|
||
<p>生成的 GLB 模型可直接用于 Web AR 体验，让消费者在购买前「把产品放在家里看看」。</p>
|
||
</div>
|
||
</div>
|
||
<div class="card usecase-card">
|
||
<span class="usecase-num">06</span>
|
||
<div>
|
||
<h3>已有模型重新贴图</h3>
|
||
<p>Shape-conditioned Texture Generation 功能：给已有的 3D 网格 + 参考图，重新生成纹理贴图。</p>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Hardware Requirements -->
|
||
<section class="section">
|
||
<h2 class="section-title">硬件要求</h2>
|
||
<p class="section-desc">本地部署所需的最低配置</p>
|
||
|
||
<div class="card">
|
||
<table class="req-table">
|
||
<thead>
|
||
<tr><th>组件</th><th>要求</th><th>说明</th></tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>操作系统</td>
|
||
<td><span class="req-val">Linux</span></td>
|
||
<td>不支持 macOS / Windows（CUDA 依赖）</td>
|
||
</tr>
|
||
<tr>
|
||
<td>GPU</td>
|
||
<td><span class="req-val">NVIDIA ≥ 24GB VRAM</span></td>
|
||
<td>官方验证 A100 / H100，RTX 4090 (24GB) 理论可用</td>
|
||
</tr>
|
||
<tr>
|
||
<td>CUDA</td>
|
||
<td><span class="req-val">12.4</span></td>
|
||
<td>需配合 PyTorch 2.6.0</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Python</td>
|
||
<td><span class="req-val">3.8+</span></td>
|
||
<td>推荐用 Conda 管理环境</td>
|
||
</tr>
|
||
<tr>
|
||
<td>内存</td>
|
||
<td><span class="req-val">≥ 32GB RAM</span></td>
|
||
<td>处理高分辨率时需要充足内存</td>
|
||
</tr>
|
||
<tr>
|
||
<td>磁盘</td>
|
||
<td><span class="req-val">≥ 30GB</span></td>
|
||
<td>模型权重 + 依赖库</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Deployment Options -->
|
||
<section class="section">
|
||
<h2 class="section-title">部署方案</h2>
|
||
<p class="section-desc">根据使用频率和预算选择最合适的方案</p>
|
||
|
||
<div class="card">
|
||
<div class="deploy-option">
|
||
<div class="deploy-icon">💻</div>
|
||
<div>
|
||
<h4>方案 A：本地 GPU 服务器</h4>
|
||
<p>自有 NVIDIA GPU 机器，一次安装长期使用。适合高频批量生成。</p>
|
||
<span class="deploy-cost">一次性投入，无后续费用</span>
|
||
</div>
|
||
</div>
|
||
<div class="deploy-option">
|
||
<div class="deploy-icon">☁️</div>
|
||
<div>
|
||
<h4>方案 B：云 GPU 按需租用</h4>
|
||
<p>RunPod / Vast.ai / Lambda Labs 租 A100 或 H100，用完即停。适合偶尔使用或测试阶段。</p>
|
||
<span class="deploy-cost">A100: ~$1.5/h | H100: ~$3/h</span>
|
||
</div>
|
||
</div>
|
||
<div class="deploy-option">
|
||
<div class="deploy-icon">🐳</div>
|
||
<div>
|
||
<h4>方案 C：Docker 一键部署</h4>
|
||
<p>社区提供的 Docker 镜像，免去环境配置烦恼。需要宿主机有 NVIDIA GPU + nvidia-docker。</p>
|
||
<div class="code-block" style="margin-top: 0.75rem;">
|
||
<span class="code-cmd">docker run</span> --gpus all -p 3000:3000 camenduru/tostui-trellis2
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Quick Install -->
|
||
<section class="section">
|
||
<h2 class="section-title">安装命令</h2>
|
||
<p class="section-desc">GPU 到位后，按以下步骤部署</p>
|
||
|
||
<div class="code-block">
|
||
<span class="code-comment"># 1. 进入源码目录</span><br>
|
||
<span class="code-cmd">cd</span> source/<br><br>
|
||
<span class="code-comment"># 2. 创建环境并安装全部依赖</span><br>
|
||
<span class="code-cmd">.</span> ./setup.sh --new-env --basic --flash-attn --nvdiffrast --nvdiffrec --cumesh --o-voxel --flexgemm<br><br>
|
||
<span class="code-comment"># 3. 启动 Web UI</span><br>
|
||
<span class="code-cmd">python</span> app.py<br><br>
|
||
<span class="code-comment"># 浏览器打开 http://localhost:7860 即可使用</span>
|
||
</div>
|
||
</section>
|
||
|
||
<div class="divider"></div>
|
||
|
||
<!-- Tech Architecture -->
|
||
<section class="section">
|
||
<h2 class="section-title">技术架构</h2>
|
||
<p class="section-desc">核心创新点</p>
|
||
|
||
<div class="grid-3">
|
||
<div class="card">
|
||
<h3>O-Voxel 表示</h3>
|
||
<p>全新的「无场」稀疏体素结构，使用 Flexible Dual Grid 同时编码几何和外观，绕开了传统 SDF / Flexicubes 的拓扑限制。</p>
|
||
</div>
|
||
<div class="card">
|
||
<h3>SC-VAE 压缩</h3>
|
||
<p>稀疏 3D VAE 实现 16 倍空间下采样。1024³ 分辨率仅需约 9,600 个 latent token，大幅降低计算量。</p>
|
||
</div>
|
||
<div class="card">
|
||
<h3>Flow Matching DiT</h3>
|
||
<p>基于 Vanilla Diffusion Transformer 的生成器，4B 参数规模，在保持高质量的同时实现极快的推理速度。</p>
|
||
</div>
|
||
</div>
|
||
</section>
|
||
|
||
<!-- Links -->
|
||
<div class="links">
|
||
<a class="link-btn primary" href="https://github.com/microsoft/TRELLIS.2" target="_blank">GitHub 仓库</a>
|
||
<a class="link-btn" href="https://huggingface.co/spaces/microsoft/TRELLIS.2" target="_blank">在线 Demo</a>
|
||
<a class="link-btn" href="https://microsoft.github.io/TRELLIS.2/" target="_blank">论文 & 官网</a>
|
||
<a class="link-btn" href="https://huggingface.co/microsoft/TRELLIS.2-4B" target="_blank">模型权重</a>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
<footer>
|
||
<p>TRELLIS.2 by Microsoft Research · MIT License · 本页仅做项目调研用途</p>
|
||
<p style="margin-top:0.5rem;"><a href="https://arxiv.org/abs/2512.14692">arXiv: 2512.14692</a></p>
|
||
</footer>
|
||
|
||
</body>
|
||
</html>
|