14 citations · 28 across the 88 of their papers we have counts for
59 papers · 1 filter
VARI: Robust Text-to-Video Generation via Group-Structured Intent Alignment
Mingxuan Zhu, Hohei Chan, Shiyuan Liu +2
Modern text-to-video (T2V) models generate visually compelling videos, yet meaning-preserving changes in prompt wording can substantially alter semantic realization and overall qua…
FlowCred: Hierarchical Credit Assignment and Functional Coordination for Joint Text-to-Video Post-Training
Mingxuan Zhu, Yanwei Huang, Shiyuan Liu +4
Prompt optimization and generator post-training are two complementary ways to improve text-to-video (T2V) generation, and recent systems increasingly train both modules jointly. Ho…
PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary
Xinlong Chen, Weihong Lin, Yuanxing Zhang +9
Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target short videos, extending them…
HiRAE: Hierarchical Representation Autoencoding with Residual Budgets
Xuanyu Zhu, Yan Bai, Yang Shi +5
Pretrained visual representations support image generation, but may not fully preserve the fine-grained details needed for faithful reconstruction. Meanwhile, intermediate encoder…
Think Before You Score: Thinking Reward Model for Visual Generation
Xuehai Bai, Zhenchen Tang, Yang Shi +9
Visual reward models are essential for evaluating and improving visual generation models, yet existing approaches typically map task conditions and candidate outputs directly to sc…
From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models
Meng Luo, Yicheng Liu, Jiahao Wang +5
Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with video--executing symbolic rules, re…
Human-Centric Image Captioning with Subject-Centered Spatial Understanding
Bozhou Li, Jiahang Zhang, Yue Ding +11
While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural hallucinations in human-centric sc…
PercepCap: Video Captioner with Structured Spatio-Temporal Perception
Yifan Xu, Zihao Wang, Zhixiao Wang +6
Video captioning requires fine-grained spatio-temporal understanding of videos, including spatial perception of where objects are located and temporal perception of when events occ…
OmniPack: Unified Token Compression for Efficient Omni-modal Large Language Models
Wanshun Su, Yang Shi, Feihu Liu +10
Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly redundant visual and audio tok…
CAPE-T2V: Captioner-Anchored Prompt Enhancement toward Two-Sided Conditioning Alignment in Text-to-Video Generation
Yizhuo Jia, Jingyun Hua, Yuanxing Zhang
Text-to-video (T2V) diffusion transformers (DiTs) are trained with detailed video captions, whereas inference often relies on user prompts rewritten by a prompt enhancer (PE). Prio…
Beacon: Knowing When and How to Perform Agentic Visual Reasoning
Qixun Wang, Yang Shi, Letian Cheng +11
The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks. We rethink agentic visual reasoning th…
RefCaptioner: Multi-Reference Image-Grounded Video Captioning
Tengfei Liu, Yang Shi, Yuran Wang +16
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-…
ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships
Xinyu Liu, Shihao Li, Weihong Lin +10
Recent diffusion-based video generation models have made significant progress in multi-reference image-conditioned video editing. However, existing methods still struggle to coordi…
KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation
Yuqi Tang, Tengfei Liu, Yizheng Lai +18
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-ke…
MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
Xiaohan Zhang, Yuqing Wen, Junlin Chen +9
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mai…
AVSCap: Orchestrating Audio-Visual Synergy for Omni-modal Video Captioning
Yanghai Wang, Jiahao Wang, Jiafu Tang +9
Omni-modal video captioning is not merely combining visual captioning with audio transcription: a useful caption must describe how visual actions, speech, music, and sound effects…
CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales
Xinlong Chen, Jiafu Tang, Yue Ding +12
Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can ob…
ARGUS: Stacked Multi-View Identity Mosaic Injection for Subject-Preserving Video Generation
Zijie Meng, Jiwen Liu, Yufei Liu +5
Subject-preserving video generation is not solved by frontal-face similarity alone: a generated person must remain recognizable across motion, large viewpoint changes, expression s…
CoVEBench: Can Video Editing Models Handle Complex Instructions?
Jiangtao Wu, Jiaming Wang, Yiwen He +7
While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt…
OmniCap-IF: Benchmarking and Improving Instruction Following Abilities for Omni-Video Captioning
Jiahao Wang, An Ping, Yanghai Wang +13
While Omni-modal Large Language Models (OLLMs) have demonstrated impressive capabilities in jointly processing audio and visual streams, their ability to strictly adhere to complex…
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV
Tengfei Liu, Yang Shi, Xuanyu Zhu +17
Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing b…
OpenWorldLib: A Unified Codebase and Definition of Advanced World Models
DataFlow Team, Bohan Zeng, Daili Hua +39
World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we…
Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
Yuqi Tang, Yang Shi, Zhuoran Zhang +21
Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural dist…
Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling
Xuehai Bai, Yang Shi, Yi-Fan Zhang +7
Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, existing benchmarks often fai…