Flow Matching Reinforcement for 3D Mesh Generation via Dynamic Homing Optimization
Flow matching is central to 3D generation, yet in practice its reinforcement learning (RL) methods are largely adapted from 2D visual generation. Representative DPO-, GRPO-, and NFT-style objectives, when applied to negative trajectories, mainly steer predicted velocities away from the corresponding directions without explicitly specifying a target velocity field toward preferred samples. In 3D generation, constrained by pretrained model capabilities, rollout diversity, and reward-distribution complexity, directly applying these RL methods yields limited gains in geometric quality. We introduce a forward-process RL method \textbf{Dynamic Homing Optimization (DHO)}, which reformulates nega...
| Authors | Zhen Zhou, Zhiwei Ning, Puhua Jiang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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