8 papers · 1 filter
Generative World Simulation with DAO and JING
XGEN Team, Guohao Cai, Haoxuan Che +16
We present a Generative World Simulation framework that couples persistent shared world state with continuous first-person audiovisual generation. The framework integrates DAO, a c…
The Past Frames the Future: Memory for Autoregressive Video Generation
Harold Haodong Chen, Rongjin Guo, Disen Lan +22
Advances in generative models have improved video fidelity, enabling long-horizon generation, interactive world modeling, and evolving visual environments. Autoregressive (AR) vide…
From Dense Prediction to Visual Editing: Structured Supervision for Unified Image and Video Creation
Zhefan Rao, Bin Zou, Xuanhua He +5
Unified image and video creation requires a model to follow diverse instructions while preserving identity, geometry, and temporal structure from visual context. However, semantic-…
InsEdit: Towards Instruction-based Visual Editing via Data-Efficient Video Diffusion Models Adaptation
Zhefan Rao, Bin Zou, Haoxuan Che +5
Instruction-based video editing is a natural way to control video content with text, but adapting a video generation model into an editor usually appears data-hungry. At the same t…
VisionDirector: Vision-Language Guided Closed-Loop Refinement for Generative Image Synthesis
Meng Chu, Senqiao Yang, Haoxuan Che +8
Generative models can now produce photorealistic imagery, yet they still struggle with the long, multi-goal prompts that professional designers issue. To expose this gap and better…
ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement
Zhefan Rao, Liya Ji, Yazhou Xing +6
Text-to-video (T2V) generation has gained significant attention recently. However, the costs of training a T2V model from scratch remain persistently high, and there is considerabl…
ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration
Yuen-Fui Lau, Tianjia Zhang, Zhefan Rao +1
We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. Our method involves repairing a single degraded input…
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models
Qiang Wen, Zhefan Rao, Yazhou Xing +1
Enhancing a low-light noisy RAW image into a well-exposed and clean sRGB image is a significant challenge for modern digital cameras. Prior approaches have difficulties in recoveri…