Hongliang Wang

Yihong Guo, Youwei Lyu, Jiajun Tang et al.

Reasoning photo retouching has gained significant traction, requiring models to analyze image defects, give reasoning processes, and execute precise retouching enhancements. However, existing approaches often rely on non-differentiable external software, creating optimization barriers and suffering from high parameter redundancy and limited generalization. To address these challenges, we propose VeraRetouch, a lightweight and fully differentiable framework for multi-task photo retouching. We employ a 0.5B Vision-Language Model (VLM) as the central intelligence to formulate retouching plans based on instructions and scene semantics. Furthermore, we develop a fully differentiable Retouch Renderer that replaces external tools, enabling direct end-to-end pixel-level training through decoupled control latents for lighting, global color, and specific color adjustments. To overcome data scarcity, we introduce AetherRetouch-1M+, the first million-scale dataset for professional retouching, constructed via a new inverse degradation workflow. Furthermore, we propose DAPO-AE, a reinforcement learning post-training strategy that enhances autonomous aesthetic cognition. Extensive experiments demonstrate that VeraRetouch achieves state-of-the-art performance across multiple benchmarks while maintaining a significantly smaller footprint, enabling mobile deployment. Our code and models are publicly available at https://github.com/OpenVeraTeam/VeraRetouch.

Muxi Chen, Yi Liu, Jian Yi et al.

In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis. Our framework categorizes evaluations into two distinct groups: first, focusing on image qualities such as aesthetics and realism, and second, examining text conditions through concept coverage and fairness. We introduce an innovative aesthetic score prediction model that assesses the visual appeal of generated images and unveils the first dataset marked with low-quality regions in generated human images to facilitate automatic defect detection. Our exploration into concept coverage probes the model's effectiveness in interpreting and rendering text-based concepts accurately, while our analysis of fairness reveals biases in model outputs, with an emphasis on gender, race, and age. While our study is grounded in human imagery, this dual-faceted approach is designed with the flexibility to be applicable to other forms of image generation, enhancing our understanding of generative models and paving the way to the next generation of more sophisticated, contextually aware, and ethically attuned generative models. Code and data, including the dataset annotated with defective areas, are available at \href{https://github.com/cure-lab/EvaluateAIGC}{https://github.com/cure-lab/EvaluateAIGC}.

Lang Zhou, Zhitai Zhang, Hongliang Wang

Rover localization is one of the perquisites for large scale rover exploration. In NASA's Mars 2020 mission, the Ingenuity helicopter is carried together with the rover, which is capable of obtaining high-resolution imagery of Mars terrain, and it is possible to perform localization based on aerial-to-ground (A2G) imagery correspondence. However, considering the low-texture nature of the Mars terrain, and large perspective changes between UAV and rover imagery, traditional image matching methods will struggle to obtain valid image correspondence. In this paper we propose a novel pipeline for Mars rover localization. An algorithm combing image-based rock detection and rock distribution pattern matching is used to acquire A2G imagery correspondence, thus establishing the rover position in a UAV-generated ground map. Feasibility of this method is evaluated on sample data from a Mars analogue environment. The proposed method can serve as a reliable assist in future Mars missions.