3d-motion

WorldFlow 是一个生成式世界模型，通过连续位移 token 与 Mixture-of-Transformers 骨干联合预测未来视频和 3D 轨迹，在多个基准上显著降低了位移误差，并发布了包含 1,678 个样本的 WorldFlowBench 评测集。

NVIDIA 的论文提出一种通过交叉注意力连接轻量 3D 运动模型与预训练视频生成器的方法，可为任意骨骼骨架同步生成视频与可回放的 3D 运动，并估计相机以实现运动与场景配准，支持视频与运动的双向转换。

HarmoHOI is a unified diffusion framework that jointly generates synchronized multi-view hand-object interaction videos and globally aligned 3D point tracks, achieving state-of-the-art performance in visual quality, motion plausibility, and multi-view geometric consistency.

AFUN proposes an affordance foundation model that predicts functional masks and 3D motion curves from RGB-D observations and language descriptions, enabling generalizable robot manipulation across diverse environments. The model outperforms baselines on multiple benchmarks and can be deployed for real-world tasks without fine-tuning.

PhyMotion proposes a physics-grounded reward system that evaluates kinematic plausibility, contact consistency, and dynamic feasibility of human motion in generated videos, achieving stronger correlation with human judgment and improving motion realism in RL-based post-training.