2 citations · 3 across the 17 of their papers we have counts for
18 papers
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-…
DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment
Xinyu Geng, Xuanhua He, Sixiang Chen +7
Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward rei…
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
Hongyu Liu, Chun Wang, Feng Gao +6
We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR video generators can produce…
ICDepth: Taming Video Diffusion Models for Video Depth Estimation via In-Context Conditioning
Xuanhua He, Jiaxin Xie, Mingzhe Zheng +1
Monocular video depth estimation requires temporal consistency, geometric accuracy, and generalization across diverse scenarios, yet existing methods struggle to achieve all three…
GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillation
Sixiang Chen, Zhaohu Xing, Tian Ye +7
Open-ended image generation is no longer a simple prompt-to-image problem. High-quality generation often requires an agent to combine a model's internal generative ability with ext…
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…
Manifold-Aware Exploration for Reinforcement Learning in Video Generation
Mingzhe Zheng, Weijie Kong, Yue Wu +9
Group Relative Policy Optimization (GRPO) methods for video generation like FlowGRPO remain far less reliable than their counterparts for language models and images. This gap arise…
WildActor: Unconstrained Identity-Preserving Video Generation
Qin Guo, Tianyu Yang, Xuanhua He +5
Production-ready human video generation requires digital actors to maintain strictly consistent full-body identities across dynamic shots, viewpoints and motions, a setting that re…
InnoAds-Composer: Efficient Condition Composition for E-Commerce Poster Generation
Yuxin Qin, Ke Cao, Haowei Liu +13
E-commerce product poster generation aims to automatically synthesize a single image that effectively conveys product information by presenting a subject, text, and a designed styl…
Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark
Ke Cao, Xuanhua He, Xueheng Li +7
Pansharpening aims to generate high-resolution multi-spectral images by fusing the spatial detail of panchromatic images with the spectral richness of low-resolution MS data. Howev…
Kling-MotionControl Technical Report
Kling Team, Jialu Chen, Yikang Ding +21
Character animation aims to generate lifelike videos by transferring motion dynamics from a driving video to a reference image. Recent strides in generative models have paved the w…
LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models
Yuxiang Shen, Hailong Huang, Zhenkun Gao +6
Multimodal Large Language Models (MLLMs) are shifting towards "Thinking with Images" by actively exploring image details. While effective, large-scale training is computationally e…
Hybrid Fusion: One-Minute Efficient Training for Zero-Shot Cross-Domain Image Fusion
Ran Zhang, Xuanhua He, Liu Liu
Image fusion seeks to integrate complementary information from multiple sources into a single, superior image. While traditional methods are fast, they lack adaptability and perfor…
ContextFlow: Training-Free Video Object Editing via Adaptive Context Enrichment
Yiyang Chen, Xuanhua He, Xiujun Ma +1
Training-free video object editing aims to achieve precise object-level manipulation, including object insertion, swapping, and deletion. However, it faces significant challenges i…
Controllable Video Generation: A Survey
Yue Ma, Kunyu Feng, Zhongyuan Hu +19
With the rapid development of AI-generated content (AIGC), video generation has emerged as one of its most dynamic and impactful subfields. In particular, the advancement of video…
FullDiT2: Efficient In-Context Conditioning for Video Diffusion Transformers
Xuanhua He, Quande Liu, Zixuan Ye +7
Fine-grained and efficient controllability on video diffusion transformers has raised increasing desires for the applicability. Recently, In-context Conditioning emerged as a power…
UNIC: Unified In-Context Video Editing
Zixuan Ye, Xuanhua He, Quande Liu +7
Recent advances in text-to-video generation have sparked interest in generative video editing tasks. Previous methods often rely on task-specific architectures (e.g., additional ad…
GameGen-X: Interactive Open-world Game Video Generation
Haoxuan Che, Xuanhua He, Quande Liu +2
We introduce GameGen-X, the first diffusion transformer model specifically designed for both generating and interactively controlling open-world game videos. This model facilitates…