2 citations · 3 across the 11 of their papers we have counts for
12 papers · 1 filter
InstructVVT: Instruction-Driven Video Virtual Try-On without Auxiliary Spatial Priors
Dingbao Shao, Song Wu, Xinyu Chen +18
Video virtual try-on is a highly constrained editing task requiring the precise replacement of a target person's clothing while strictly preserving the original video's spatial str…
MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation
Dewei Zhou, Xinyu Huang, Xun Wang +6
Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot direct…
REC-RL: Referring expression counting via Gaussian and range-based reward optimization
Hui Liu, Yunlai Teng, Kunlong Bai +4
Referring expression counting (REC) is an intention-driven task that requires context-aware visual reasoning. While recent vision-language models incorporate language for visual un…
Tuning-free Instruction-based Video Editing Via Structural Noise Initialization and Guidance
Song Wu, Xinyu Chen, Qian Wang +3
Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model training, they often underutilize the…
GeoTikzBridge: Advancing Multimodal Code Generation for Geometric Perception and Reasoning
Jiayin Sun, Caixia Sun, Boyu Yang +7
Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. However, they struggle to perceive fine-grained geometric structu…
Revisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive Learning
Zelin Zang, Yehui Yang, Fei Wang +2
Unsupervised Domain Adaptation (UDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain but often suffers from severe domain and scale gaps that…
RewardDance: Reward Scaling in Visual Generation
Jie Wu, Yu Gao, Zilyu Ye +9
Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It pr…
Seedream 4.0: Toward Next-generation Multimodal Image Generation
Team Seedream, :, Yunpeng Chen +48
We introduce Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image compositi…
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Yu Gao, Haoyuan Guo, Tuyen Hoang +41
Notable breakthroughs in diffusion modeling have propelled rapid improvements in video generation, yet current foundational model still face critical challenges in simultaneously b…
Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
Chao Liao, Liyang Liu, Xun Wang +7
Recent progress in unified models for image understanding and generation has been impressive, yet most approaches remain limited to single-modal generation conditioned on multiple…
Seedream 3.0 Technical Report
Yu Gao, Lixue Gong, Qiushan Guo +28
We present Seedream 3.0, a high-performance Chinese-English bilingual image generation foundation model. We develop several technical improvements to address existing challenges in…
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation Model
Lixue Gong, Xiaoxia Hou, Fanshi Li +25
Rapid advancement of diffusion models has catalyzed remarkable progress in the field of image generation. However, prevalent models such as Flux, SD3.5 and Midjourney, still grappl…