arXiv Computer Vision
6d ago

SCOPE: Semantic Bounded Credit Optimization for Prompt Engineering in Text-to-image Generation

SCOPE introduces a reinforcement learning approach for text‑to‑image prompt engineering that respects semantic spans such as objects, counts, attributes, and relations. By pooling policy log‑probability shifts within each semantic span, the method assigns a shared coefficient to token losses while using a single advantage per completion, eliminating the need for separate span rewards or extra image renders. Experiments on Qwen‑Image, SD3, and FLUX.1‑dev show average relative gains of 9.48% on GeneVal and 7.62% on T2I‑CompBench compared to vanilla GRPO, with ablation studies confirming the benefit of semantic boundaries and pooling.

By Yufei Jiao, Jinfa Huang, Zeyue Xue, Tianyu Xie, Yuexiao Ma, Rongrong Ji, Xiawu Zheng, Jiebo Luo
arXiv Computer Vision
Oct 12

MotionRFT: Unified Reinforcement Fine-Tuning for Text-to-Motion Generation

MotionRFT introduces a reinforcement fine‑tuning framework for text‑to‑motion generation that unifies heterogeneous motion representations into a shared semantic space using the MotionReward model. The framework includes a fine‑grained, memory‑efficient fine‑tuning method called EasyTune, which optimizes step‑wise denoising steps to reduce computational overhead. Experiments show significant improvements in FID, R‑Precision, and memory usage across several motion models.

By Xiaofeng Tan, Wanjiang Weng, Hongsong Wang, Fang Zhao, Xin Geng, Liang Wang
arXiv AI
Aug 7

iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

arXiv:2608. 06161v1 Announce Type: new Abstract: Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints.

By Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel