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文章主要从多张输入视角合成新视角图像，使用CNN预测混合权重，来进行图像混合和合成。
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/09/17/hallo/" title="hallo">hallo</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-09-17T13:50:08.000Z" title="Created 2024-09-17 21:50:08">2024-09-17</time></span></div><div class="content">文章没有采取显示中间表达或者潜空间分离特征分离的方式完成说话人头的人物，而是使用端到端训练diffusion的方式，解决对齐问题，同时使模型具备音频控制嘴唇和表情的能力。
Hierarchical Audio-Driven Visual Synthesis
使用预训练好的人脸编码器提取身份特征cexpc_{exp}cexp这些特征被输送到cross-attention模块保证最终输出与输入肖像保持身份一致。
使用wac2vec作为音频特征编码器，将最后12层输出作为音频特征。一次处理长度为5s的音频，得到对应帧的特征，并使用三个先行层特征进行转换。
如下图使用多层级音频-视觉cross attention架构来促进模型学习音频和视觉信息的特征。首先获得每一帧的嘴唇、表情和姿势掩码，接着将cross-attention应用于latents和音频特征，接着利用掩码对结果进行处理，最后将三个部分卷积后，通过可学习的融合模块进行融合。
</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/09/13/RealisDance/" title="RealisDance">RealisDance</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-09-13T06:48:02.000Z" title="Created 2024-09-13 14:48:02">2024-09-13</time></span></div><div class="content">目前已有方法缺陷
姿态输入损坏时可能会导致生成失败
使用DWPose生成的序列存在模糊和不真实的情况
如果姿势序列不够平滑生成的视频不稳定
我认为还存在的问题
当前框架的缺点来源于骨架的空间信息和细节(脸或手)信息的缺失，空间信息本文的SMPLX-CS是一个解决方案，手部采用检测MANO并投影解决。我认为可以有以下改进，一是脸部是最重要的部分，需要想一个新的编码方式，从crop的人脸图中提取关键点特征作为人脸prompt；二是手部看有没有改进的可能；在自己设计的方案中目前手、身体采用直接相加的方法，并没有进行有效的融合；三是可以考虑添加局部loss、GAN Loss来使生成的结果更加真实；四是可以考虑在第二训练阶段训练view-attention时使用3D关键点，使得允许在小范围内生成视点可控的结果。
具体上文章使用HaMeR提供手部准确的3D和深度信息，使得pipeline能够生成真实的手部。将时间模块引入到pose guidance network中，使得pose指导的视频生成结果更加平滑。在训练时打乱增强pose，提高生成的鲁棒性和视频的平滑性。
文章总共设计了3种姿势输入 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/09/12/LivePortrait/" title="LivePortrait">LivePortrait</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-09-12T06:53:11.000Z" title="Created 2024-09-12 14:53:11">2024-09-12</time></span></div><div class="content">Face Vid2Vid
该框架从驱动视频序列中提取运动特征，并驱动一张静止的肖像。原始框架包含一个外观提取器mathcalFmathcal{F}mathcalF，一个隐式关键点检测器mathcalLmathcal{L}mathcalL，一个头部姿态估计网络mathcalHmathcal{H}mathcalH，一个表情变形网络Δ\DeltaΔ，一个弯曲场估计其mathcalWmathcal{W}mathcalW和一个生成器mathcalGmathcal{G}mathcalG。其中mathcalFmathcal{F}mathcalF能将原图像s映射为一个3D外观体特征你fsf_sfs。源图像和驱动的3D关键点分别如下：
{xs=xc,sRs+δs+ts,xs=xc,sRd+δd+td\begin{cases}x_s=x_{c,s}R_s+{\delta}_s+t_s, \\x_s=x_{c,s}R_d+{\delta}_d+t_d\end{cases}
{xs=xc,sRs+δs+ts,xs=xc,sRd+δd+td
其中xsx_sxs和xdx_dxd为3D隐 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/09/05/%E4%BA%BA%E4%BD%93%E5%85%B3%E9%94%AE%E7%82%B9/" title="人体关键点">人体关键点</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-09-05T01:56:16.000Z" title="Created 2024-09-05 09:56:16">2024-09-05</time></span></div><div class="content">OpenPose
输出格式包括BODY25，COCO，Face，Hand
BODY_25
joints_name = [
“Nose”, # 0 => 24 (SMPL)
“Neck”, # 1 => 12
“RShoulder”, # 2 => 17
“RElbow”, # 3 => 19
“RWrist”, # 4 => 21
“LShoulder”, # 5 => 16
“LElbow”, # 6 => 18
“LWrist”, # 7 => 20
“MidHip”, # 8 => 0
“RHip”, # 9 => 2
“RKnee”, # 10 => 5
“RAnkle”, # 11 => 8
“LHip”, # 12 => 1
“LKnee”, # 13 => 4
“LAnkle”, ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/26/StyleAvatar/" title="StyleAvatar">StyleAvatar</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-08-26T07:20:22.000Z" title="Created 2024-08-26 15:20:22">2024-08-26</time></span></div><div class="content">Abstract
对StyleGAN进行扩展，使用组合表示滑动窗口增强。将肖像分为人脸部分、非人脸的前景部分和背景部分。
Featture Generation and Combination
特征生成部分采用两个StyleGAN生成器生成面部和背景静态特征图。面部特征图在UV空间产生，输入一个身份latent code对于不同的视频可以生成不同的特征图。对于非面部前景区域提出了StyleUNet从3DMM rendering生成像素对齐的特征图。为了适应无法控制的变化，如头发运动使用输入的时间潜在代码𝑧𝑡𝑚𝑝作为一个额外的输入。使用位置嵌入将人的身份和时间戳映射到更高的维度，以便将它们输入到我们的网络中。
特征组合部分，将三个特征图结合在一起生成最终的图像。对于面部区域，3DMM已经提供了基本的几何形状，因此我们采用延迟神经渲染（DNR）中提出的神经纹理，通过使用UV渲染从面部UV特征图中采样来生成面部特征图。对于前景和背景图用滑动窗口进行简餐保证生成特征图和输出图像之间像素对齐关系。将面部特征图和前景特征图相加适应眼镜等形状的生成。用一个卷积网络从前后景特征图预测出前景ma ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/13/AnimatableGaussian/" title="AnimatableGaussian">AnimatableGaussian</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-08-13T09:28:36.000Z" title="Created 2024-08-13 17:28:36">2024-08-13</time></span></div><div class="content">Abstract
将CNN与3DGS相结合，从视频中学习到一个参数化模板，将模板参数化到正面和背面的标准空间高斯图，其中每个像素代表一个3D高斯。具体采用Style-GAN架构的CNN网络学习pose-dependent高斯图用来建模细节的动态外观。此外引入了一个姿势投影策略，使得模型能够泛化到新颖姿势。此外引入了一个基于物理的渲染方法，将avatar解为材质和动态的环境光。
Introduction
传统的meshes和点云的显示表达方法，需要进行密集的重建建模人体集合，导致在稀疏视角、基于视频的重建失败。隐式方式如Nerf，会受到MLP低频谱的限制，难以捕捉高频细节。
将3DGS渲染到正面和背后两个视角，这两个视角几乎覆盖率标准人的全部部分。在每个视角中，模板掩码内的每一个像素被定义为一个高斯，通过其位置、方差、占用值和颜色属性，生成两个高斯图。同样，给定姿势后将两个姿势位置图作为姿势条件。这样可以借助StyleGAN-based条件生成器StyleUNet，从姿势条件生成pose-dependent高斯图。
训练完的模型难以泛化到新颖的姿势，作者提出了一个投影策略，将姿势图转化到 ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/08/10/LayerGaussian/" title="LayerGaussian">LayerGaussian</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-08-10T09:05:08.000Z" title="Created 2024-08-10 17:05:08">2024-08-10</time></span></div><div class="content">PipeLine
文章提出一个以姿势为条件的分层3DGS生成器，生成可动画的avatar，并且支持换装。整体给定身体姿势后，将其转化为位置图作为姿势条件，接着使用一个StyleUNet结构模型预测与姿势相关的3D高斯。这些3DGS从标准空间的SMPLX模板经过LBS非刚性变化得到。整体分为两阶段，在第一个阶段，通过几何约束和衣装掩码监督获得分割后的重建。在多层拟合阶段，训练了身体和衣装2个高斯。
Clothing-aware Avatar Representation
整体表示基于AnimatableGaussian。具体给定姿势参数θ\thetaθ，将其转化为位置图Mpos∈RH×W×6M_{pos}\in \mathbb{R}^{H\times W\times 6}Mpos∈RH×W×6作为条件信号，其中6为前后位置图按照通道感知拼接的结果。位置图即通过SMPLX前后投影，并将顶点按照变化姿势后的坐标系进行着色。使用这种2D参数化表示后，可以通过2D卷积神经网络预测高斯参数，来建模高质量的外观。这些位置图传入StyleUNet中生成前后高斯图Mgf,Mgb∈RH×W×CM ...</div></div></div><div class="recent-post-item"><div class="recent-post-info no-cover"><a class="article-title" href="/2024/07/16/Human4DiT/" title="Human4DiT">Human4DiT</a><div class="article-meta-wrap"><span class="post-meta-date"><i class="far fa-calendar-alt"></i><span class="article-meta-label">Created</span><time datetime="2024-07-16T02:05:02.000Z" title="Created 2024-07-16 10:05:02">2024-07-16</time></span></div><div class="content">Abstract
将UNet于控制模块几何，实现高质量自由视点和时空一致长人类视频的合成。
提出4D diffusion transformer架构，其包含3个transformer处理2D图像、时间和视点。
收集了多维度数据集，并制定训练策略来利用各种模态的数据集
提出了一种时空一致的扩散采样策略来生成一致的360度长的人类视频。
Network Structure
网络核心为一个4D diffusion transformer和控制条件注入模块。整体4D diffusion transformer采取了一个级联结构，包括2D图像、时间和视图transformer模块。此外，条件注入模块包括参考图像注入模块、SMPL注入模块、时间注入模块和视点注入模块。参考图像注入模块和SMPL注入模块利用UNet架构来确保控制条件的像素对齐注入。
4D Diffusion Transformer
输入为多视角视频latents，其可以变为5D token xt∈RV×T×H×W×Cx_t\in \mathbb{R}^{V\times T\times H\times W\times C}xt ...</div></div></div><nav id="pagination"><div class="pagination"><span class="page-number current">1</span><a class="page-number" href="/page/2/#content-inner">2</a><span class="space">…</span><a class="page-number" href="/page/5/#content-inner">5</a><a class="extend next" rel="next" href="/page/2/#content-inner"><i class="fas fa-chevron-right fa-fw"></i></a></div></nav></div><div class="aside-content" id="aside-content"><div class="card-widget card-info"><div class="is-center"><div class="avatar-img"><img src="/img/avatar.jpg" onerror="this.onerror=null;this.src='/img/friend_404.gif'" alt="avatar"/></div><div class="author-info__name">Wei Duan</div><div class="author-info__description">迷茫探索ing</div></div><div class="card-info-data site-data is-center"><a href="/archives/"><div class="headline">Articles</div><div class="length-num">43</div></a><a href="/tags/"><div class="headline">Tags</div><div class="length-num">4</div></a><a href="/categories/"><div class="headline">Categories</div><div class="length-num">0</div></a></div><a id="card-info-btn" target="_blank" rel="noopener" href="https://github.com/xxxxxx"><i class="fab fa-github"></i><span>Follow Me</span></a></div><div class="card-widget card-announcement"><div class="item-headline"><i class="fas fa-bullhorn fa-shake"></i><span>Announcement</span></div><div class="announcement_content">This is my Blog</div></div><div class="sticky_layout"><div class="card-widget card-recent-post"><div class="item-headline"><i class="fas fa-history"></i><span>Recent Post</span></div><div class="aside-list"><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/11/16/InvertAvatar/" title="InvertAvatar">InvertAvatar</a><time datetime="2024-11-16T07:37:36.000Z" title="Created 2024-11-16 15:37:36">2024-11-16</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/10/13/deepblending/" title="deepblending">deepblending</a><time datetime="2024-10-13T11:34:58.000Z" title="Created 2024-10-13 19:34:58">2024-10-13</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/09/17/hallo/" title="hallo">hallo</a><time datetime="2024-09-17T13:50:08.000Z" title="Created 2024-09-17 21:50:08">2024-09-17</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/09/13/RealisDance/" title="RealisDance">RealisDance</a><time datetime="2024-09-13T06:48:02.000Z" title="Created 2024-09-13 14:48:02">2024-09-13</time></div></div><div class="aside-list-item no-cover"><div class="content"><a class="title" href="/2024/09/12/LivePortrait/" title="LivePortrait">LivePortrait</a><time datetime="2024-09-12T06:53:11.000Z" title="Created 2024-09-12 14:53:11">2024-09-12</time></div></div></div></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>Tags</span></div><div class="card-tag-cloud"><a href="/tags/3D-Human-Diffusion/" style="font-size: 1.1em; color: #999">3D Human Diffusion</a> <a href="/tags/%E8%B0%83%E7%A0%94/" style="font-size: 1.1em; color: #999">调研</a> <a href="/tags/Human-Motion/" style="font-size: 1.5em; color: #99a9bf">Human Motion</a> <a href="/tags/SDS/" style="font-size: 1.1em; color: #999">SDS</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>Archives</span><a class="card-more-btn" href="/archives/" title="View More">
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