2 citations · 3 across the 14 of their papers we have counts for
14 papers
MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings
Zijie Li, Yichun Shi, Jingxiang Sun +8
We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM) to predict semantic visual e…
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability
Bingchen Zhao, Qiushan Guo, Ye Wang +3
We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By emp…
MTV: Revisiting Multi-Task Visual Representation Learning
Shangzhe Di, Zhonghua Zhai, Weidi Xie
Current visual representation learning remains bifurcated: vision-language models (e.g., CLIP) excel at global semantic alignment but lack spatial precision, while self-supervised…
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…
SeedEdit 3.0: Fast and High-Quality Generative Image Editing
Peng Wang, Yichun Shi, Xiaochen Lian +5
We introduce SeedEdit 3.0, in companion with our T2I model Seedream 3.0, which significantly improves over our previous SeedEdit versions in both aspects of edit instruction follow…
Universal Video Temporal Grounding with Generative Multi-modal Large Language Models
Zeqian Li, Shangzhe Di, Zhonghua Zhai +3
This paper presents a computational model for universal video temporal grounding, which accurately localizes temporal moments in videos based on natural language queries (e.g., que…
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…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Large Models
Chen Ju, Haicheng Wang, Haozhe Cheng +6
Vision-Language Large Models (VLMs) recently become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and dela…
Tunnel Try-on: Excavating Spatial-temporal Tunnels for High-quality Virtual Try-on in Videos
Zhengze Xu, Mengting Chen, Zhao Wang +6
Video try-on is a challenging task and has not been well tackled in previous works. The main obstacle lies in preserving the details of the clothing and modeling the coherent motio…
Cell Variational Information Bottleneck Network
Zhonghua Zhai, Chen Ju, Jinsong Lan +1
In this work, we propose Cell Variational Information Bottleneck Network (cellVIB), a convolutional neural network using information bottleneck mechanism, which can be combined wit…
Wear-Any-Way: Manipulable Virtual Try-on via Sparse Correspondence Alignment
Mengting Chen, Xi Chen, Zhonghua Zhai +4
This paper introduces a novel framework for virtual try-on, termed Wear-Any-Way. Different from previous methods, Wear-Any-Way is a customizable solution. Besides generating high-f…
Turbo: Informativity-Driven Acceleration Plug-In for Vision-Language Models
Chen Ju, Haicheng Wang, Zeqian Li +4
Vision-Language Large Models (VLMs) have become primary backbone of AI, due to the impressive performance. However, their expensive computation costs, i.e., throughput and delay, i…