Latent Visual Diffusion Reasoning with Monte Carlo Tree Search
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 accu...
| Authors | Xirui Teng, Nan Xi, Junsong Yuan |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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