面向高约束人体动作生成的检索引导扩散噪声优化
Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization
把「检索到的参考动作噪声」作为扩散噪声优化的初始化，并用奖励引导掩码与随机噪声混合，这一免训练思路可直接迁移到其他受强约束的生成任务（如布局约束的图像/视频生成、轨迹约束的动画）。
原文依据 A better initialization for diffusion noise is then obtained via a reward-guided mask that combines random noise with retrieved noise.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论生成满足定制化零样本目标函数的人体动作，从而支持可控角色动画与虚拟智能体行为合成等应用，是一项关键能力。现有方法虽能处理许多未见过的约束，但在具有极大挑战性的时空限制任务上会失败，例如存在严重空间障碍或指定行走步数。为使动作生成器胜任这些高约束任务，我们提出一种基于免训练扩散噪声优化框架的检索引导方法。其核心思想是在大规模动作数据集中搜索可能满足困难约束的引导信息。我们引入关系型任务解析，对目标约束进行分组并识别出需要由检索到的参考来处理的困难约束。随后通过奖励引导掩码将随机噪声与检索噪声相结合，获得扩散噪声的更优初始化。从该改进初始化出发优化扩散噪声，我们成功求解了高约束生成任务。借助大语言模型进行关系型任务解析，整个框架进一步能够自动推理应当检索什么，在免训练优化方案下提升了移动智能体的智能性。项目主页：https://hanchaoliu.github.io/RetrievalGuidedDNO/
英文原文摘要 1369 字符
Generating human motion that satisfies customized zero-shot goal functions, enabling applications such as controllable character animation and behavior synthesis for virtual agents, is a critical capability. While current approaches handle many unseen constraints, they fail on tasks with very challenging spatiotemporal restrictions, such as severe spatial obstacles or specified numbers of walking steps. To equip motion generators for these highly constrained tasks, we present a retrieval-guided method built on the training-free diffusion noise optimization framework. The key idea is to search within large motion datasets for guidance that can potentially satisfy difficult constraints. We introduce relational task parsing to group target constraints and identify the difficult ones to be handled by retrieved reference. A better initialization for diffusion noise is then obtained via a reward-guided mask that combines random noise with retrieved noise. By optimizing diffusion noise from this improved initialization, we successfully solve highly constrained generation tasks. By leveraging LLM for relational task parsing, the whole framework is further enabled to automatically reason for what to retrieve, improving the intelligence of moving agents under a training-free optimization scheme. Project page: https://hanchaoliu.github.io/RetrievalGuidedDNO/