让动作评估模型像人一样一步步推理，还能画出思考路径。
Latent Visual Diffusion Reasoning with Monte Carlo Tree Search
- 用关键点引导的蒙特卡洛树搜索，模拟视觉推理过程。
- 在4个不同领域的数据集上表现优异，准确率高于基线模型。
- 适合需要解释性的高风险场景，如手术评估和体育训练分析。
精细动作活动（如体育运动、外科手术）的分析不仅需要识别视觉模式，还需进行逐步的视觉推理以得出最终判断。尽管近期动作质量评估取得了显著进展，但现有模型仍为黑箱，无法显式揭示其判断背后的推理过程。为此，我们提出潜空间视觉扩散推理（LVDR）框架，通过关键点引导的蒙特卡洛树搜索（MCTS）建模并可视化潜空间中的视觉推理过程。LVDR不仅能实现更精准的动作评估，还能揭示对最终评价起关键作用的视觉推理序列。在涵盖多个体育与外科领域的四个数据集上的大量实验表明，LVDR在定量性能上达到竞争力水平，同时提供可解释的视觉推理轨迹以支持最终预测。源代码与模型可通过以下链接获取：https://github.com/XiruiTeng/LVDR_Official.git。
原文摘要 · Abstract (English)
Analyzing fine-grained skill activities (e.g., sports, surgery) requires not only recognizing visual patterns but also performing step-by-step visual reasoning that leads to the final judgment. While recent advances in action quality assessment have achieved remarkable progress in evaluating performance, existing models remain black boxes, where they lack the ability to explicitly reveal the reasoning processes underlying their judgments. To address this limitation, we propose Latent Visual Diffusion Reasoning (LVDR), a novel framework that integrates keypoint-guided Monte Carlo Tree Search (MCTS) to model and visualize the latent visual reasoning process. LVDR not only produces more accurate skill assessments but also uncovers the critical visual reasoning sequences that contribute to the final evaluation. Extensive experiments across four datasets spanning diverse sports and surgical domains demonstrate that LVDR achieves competitive quantitative performance while providing interpretable visual reasoning trajectories leading to the final predictions. Source codes and models can be found through the following link: https://github.com/XiruiTeng/LVDR_Official.git.
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