1 citations · 1 across the 15 of their papers we have counts for
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Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding
Xinkui Zhao, Zuxin Wang, Yifan Zhang +6
The rapid development of multimodal large-language models (MLLMs) has significantly expanded the scope of visual language reasoning, enabling unified systems to interpret and descr…
S2D-ALIGN: Shallow-to-Deep Auxiliary Learning for Anatomically-Grounded Radiology Report Generation
Jiechao Gao, Chang Liu, Yuangang Li
Radiology Report Generation (RRG) aims to automatically generate diagnostic reports from radiology images. To achieve this, existing methods have leveraged the powerful cross-modal…
BindWeave: Subject-Consistent Video Generation via Cross-Modal Integration
Zhaoyang Li, Dongjun Qian, Kai Su +6
Diffusion Transformer has shown remarkable abilities in generating high-fidelity videos, delivering visually coherent frames and rich details over extended durations. However, exis…
ScenePainter: Semantically Consistent Perpetual 3D Scene Generation with Concept Relation Alignment
Chong Xia, Shengjun Zhang, Fangfu Liu +3
Perpetual 3D scene generation aims to produce long-range and coherent 3D view sequences, which is applicable for long-term video synthesis and 3D scene reconstruction. Existing met…
PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation
Wei Yao, Yunlian Sun, Chang Liu +2
Driven by advancements in motion capture and generative artificial intelligence, leveraging large-scale MoCap datasets to train generative models for synthesizing diverse, realisti…
Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
Yixian Xu, Shengjie Luo, Liwei Wang +2
Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting qual…
DiTPainter: Efficient Video Inpainting with Diffusion Transformers
Xian Wu, Chang Liu
Many existing video inpainting algorithms utilize optical flows to construct the corresponding maps and then propagate pixels from adjacent frames to missing areas by mapping. Desp…
Aquarius: A Family of Industry-Level Video Generation Models for Marketing Scenarios
Huafeng Shi, Jianzhong Liang, Rongchang Xie +3
This report introduces Aquarius, a family of industry-level video generation models for marketing scenarios designed for thousands-xPU clusters and models with hundreds of billions…
VC-LLM: Automated Advertisement Video Creation from Raw Footage using Multi-modal LLMs
Dongjun Qian, Kai Su, Yiming Tan +5
As short videos have risen in popularity, the role of video content in advertising has become increasingly significant. Typically, advertisers record a large amount of raw footage…