Zhongyi Zhang

Zhongyi Zhang, Guangyuan Wang, Li Hu et al.

Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation. However, existing approaches predominantly focus on character animation from a single reference image, substantially limiting their applicability in scenarios such as multiple character interaction animation. To fill this gap, this paper introduces MultiAnimate, a comprehensive framework that enables concurrent animation of multiple characters within a shared environment while preserving both identity consistency and spatial relationships. The framework achieves these objectives through multiple well-designed mechanisms. First, we incorporate an identity-specific reference net that enables appearance extraction from multiple reference images, distinguishing MultiAnimate from existing approaches constrained to single reference inputs. Second, we implement an identity-aware pose encoder to address the character-pose binding challenge, wherein an attention mechanism enables the network to accurately differentiate and process multiple pose sequences during generation. Third, we introduce an interaction guider module that enhances the framework's capability to handle complex inter-character interactions by leveraging character-specific mask information, serving as an optional component that refines the pose sequences. Extensive experiments and ablation analyses demonstrate our framework's superiority in multiple character animation, particularly in scenarios involving complex motion sequences.

Jingyu Xiao, Zhongyi Zhang, Haoran Hou et al.

Automated Program Repair (APR) has witnessed significant progress with the advent of Large Language Models (LLMs). However, as modern software systems increasingly expose rich graphical user interfaces, effectively leveraging visual information from bug screenshots has become essential for understanding bugs and generating accurate fixes in multimodal scenarios. Real-world issue reports frequently contain heterogeneous visual attachments including UI screenshots, IDE snapshots, GIFs, and text-centric images, each with distinct visual patterns and domain-specific semantics that impose substantial perceptual demands on MLLMs. Furthermore, bug screenshots often contain large expanses of uninformative and bug-irrelevant regions, distracting the model's attention and limiting patch diversity. To address these challenges, we propose VisualRepair, an MLLM-based framework for visual software issue repair comprising two core modules: Image Type-aware Tool Calling (ITTC), which classifies input images and dynamically invokes a tailored tool-calling chain for robust visual interpretation, and Dynamic Test-time Region Focusing (DTRF), which grounds multiple bug-related region candidates and refines them via an adaptive zoom-in and zoom-out strategy to improve fault localization and promote diverse patch generation. Extensive experiments on the SWE-bench Multimodal benchmark demonstrate that VisualRepair consistently outperforms state-of-the-art approaches. VisualRepair resolves 196 and 25 instances on the test and dev sets, respectively, surpassing the best baseline by 10 and 11 instances. These results highlight the effectiveness of type-aware visual understanding and region-focused localization for automated visual software issue repair.