Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes
Ting Yu, Yi Lin, Jun Yu, Zhenyu Lou, Qiongjie Cui
2025年份
摘要
Figure 1. The proposed GAP3DS: Gaze-informed Affordance for human pose Prediction in 3D Scenes. Compared to the state-of-the- art SIF3D [41] utilizing the position of human gaze (yellow cross), GAP3DS is able to identify the intended object (green) and infer its affordance, where both position and affordance information are used to guide the motion prediction. It therefore achieves more accurate predicted poses, and performs significant gains for those poses where human-object interaction is expected (as highlighted in circled areas).
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它引用的顶会 Paper41
- Learning Trajectory Dependencies for Human Motion PredictionWei Mao, Miaomiao Liu, Mathieu Salzmann, Hongdong LiICCV 2019 · 被引用 534 次
- PhysDiff: Physics-Guided Human Motion Diffusion ModelYe Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat 等ICCV 2023 · 被引用 414 次
- SoftGroup for 3D Instance Segmentation on Point CloudsThang Vu, Kookhoi Kim, Tung Minh Luu, Thanh Xuan Nguyen 等CVPR 2022 · 被引用 251 次
- Hand-Object Contact Consistency Reasoning for Human Grasps GenerationHanwen Jiang, Shaowei Liu, Jiashun Wang, Xiaolong WangICCV 2021 · 被引用 242 次
- Guided Motion Diffusion for Controllable Human Motion SynthesisKorrawe Karunratanakul, Konpat Preechakul, Supasorn Suwajanakorn, Siyu TangICCV 2023 · 被引用 240 次
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