Jiangning Zhang

Achieving high-fidelity 3D generation with just 1.5% of the training data could revolutionize resource allocation in 3D modeling.

Error drift in long-horizon video generation can be effectively controlled, leading to state-of-the-art synthesis quality and temporal consistency.

Interactive visualizations of LLM outputs can transform how users engage with complex information, allowing for targeted exploration without the need to sift through dense text.

A single framework can seamlessly integrate multiple advanced segmentation techniques, revolutionizing how medical image analysis is conducted.

MambaADv2 achieves superior anomaly detection by combining linear computational efficiency with advanced global and local representation modeling, setting a new standard in unsupervised learning.

SPOT-E transforms frozen VLMs into more reliable evidence readers by dynamically spotlighting critical visual information during inference.

CineDance-1M sets a new standard for open-source cinematic audio-video generation, boasting over 1 million high-quality, structured video samples that could transform the landscape of multimedia AI.

MLLMs can revolutionize video understanding by integrating watching, remembering, and reasoning into a cohesive framework that addresses long-range dependencies and sparse evidence.

Achieving top-tier identity preservation in text-to-video generation without compromising on semantic fidelity, ST-DRC redefines the standards for high-quality video synthesis.

Forget generic chatbots – now, with just 10 images and interaction examples, you can fine-tune a model to embody a specific character with a consistent persona, dialogue style, and visual identity across text and images.