Multimedia Communication and Technology

Últimos artículos

• HarmoniDPO: Video-guided Audio Generation via Preference-Optimized Diffusion

  Wenshuo Peng, Kaipeng Zhang · 13 de agosto de 2026

  Video-to-audio (V2A) generation faces significant challenges in achieving precise temporal synchronization and high perceptual quality due to the complex, ambiguous relationship between visual and auditory cues. Existing methods typically compress video inputs into single feature representations, le…

• Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

  Henglin Liu, Fangyuan Kong, Jing Wang, Yizhou Lin, Nisha Huang, Chang Liu, Xintao Wang, Pengfei Wan, Kun Gai, Xiu Li · 31 de julio de 2026

  Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly improved visual quality. However, temporally sparse artifacts such as motion collapse, object flickering, and color oversaturation remain a major ba…

• T-POP: Test-Time Personalization with Online Preference Feedback

  Zikun Qu, Min Zhang, Mingze Kong, Xiang Li, Zhiwei Shang, Zhiyong Wang, Yikun Ban, Shuang Qiu, Yao Shu, Zhongxiang Dai · 2 de junio de 2026

  Personalizing large language models (LLMs) to individual user preferences is a critical step beyond generating generically helpful responses. However, current personalization methods are ill-suited for new users, as they typically require either slow, resource-intensive fine-tuning or a substantial …

• SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models

  Xiaomeng Yang, Mengping Yang, Junyan Wang, Zhijian Zhou, Zhiyu Tan, Hao Li · 19 de mayo de 2026

  Preference learning has garnered extensive attention as an effective technique for aligning diffusion models with human preferences in visual generation. However, existing alignment approaches such as Diffusion-DPO suffer from two fundamental challenges: training instability caused by high gradient …

• Tighter Performance Theory of FedExProx

  Wojciech Anyszka, Kaja Gruntkowska, Alexander Tyurin, Peter Richt\'arik · 21 de abril de 2026

  We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal algorithms via extrapolation. In the process, we uncover a surprising flaw: its known theoretical guarantees on quadratic optimization tasks are no better than t…

• BalancedDPO: Adaptive Multi-Metric Alignment

  Dipesh Tamboli, Souradip Chakraborty, Aditya Malusare, Biplab Banerjee, Amrit Singh Bedi, Vaneet Aggarwal · 7 de abril de 2026

  Diffusion models have achieved remarkable progress in text-to-image generation, yet aligning them with human preference remains challenging due to the presence of multiple, sometimes conflicting, evaluation metrics (e.g., semantic consistency, aesthetics, and human preference scores). Existing align…

• JavisDiT: Joint Audio-Video Diffusion Transformer with Hierarchical Spatio-Temporal Prior Synchronization

  Kai Liu, Wei Li, Lai Chen, Shengqiong Wu, Yanhao Zheng, Jiayi Ji, Fan Zhou, Jiebo Luo, Ziwei Liu, Hao Fei, Tat-Seng Chua · 24 de febrero de 2026

  This paper introduces JavisDiT, a novel Joint Audio-Video Diffusion Transformer designed for synchronized audio-video generation (JAVG). Based on the powerful Diffusion Transformer (DiT) architecture, JavisDiT simultaneously generates high-quality audio and video content from open-ended user prompts…

• A Survey: Spatiotemporal Consistency in Video Generation

  Zhiyu Yin, Kehai Chen, Xuefeng Bai, Ruili Jiang, Juntao Li, Hongdong Li, Jin Liu, Yang Xiang, Jun Yu, Min Zhang · 19 de febrero de 2026

  Video generation aims to produce temporally coherent sequences of visual frames, representing a pivotal advancement in Artificial Intelligence Generated Content (AIGC). Compared to static image generation, video generation poses unique challenges: it demands not only high-quality individual frames b…

• Dual-IPO: Dual-Iterative Preference Optimization for Text-to-Video Generation

  Xiaomeng Yang, Mengping Yang, Jia Gong, Luozheng Qin, Zhiyu Tan, Hao Li · 11 de febrero de 2026

  Recent advances in video generation have enabled thrilling experiences in producing realistic videos driven by scalable diffusion transformers. However, they usually fail to produce satisfactory outputs that are aligned to users' authentic demands and preferences. In this work, we introduce Dual-Ite…

• CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering

  Hang Lv, Sheng Liang, Hao Wang, Hongchao Gu, Yaxiong Wu, Wei Guo, Defu Lian, Yong Liu, Enhong Chen · 6 de febrero de 2026

  Personalization has become crucial for adapting models to the diverse and evolving needs of users across cultural, temporal, and contextual dimensions. While existing methods often rely on centralized fine-tuning or static preference alignment within a single model, they struggle to achieve both rea…

• RPO: Fine-Tuning Visual Generative Models via Rich Vision-Language Preferences

  Hanyang Zhao, Haoxian Chen, Yucheng Guo, Genta Indra Winata, Tingting Ou, Ziyu Huang, David D. Yao, Wenpin Tang · 13 de enero de 2026

  Traditional preference tuning methods for LLMs/Visual Generative Models often rely solely on reward model labeling, which can be opaque, offer limited insights into the rationale behind preferences, and are prone to issues such as reward hacking or overfitting. We introduce Rich Preference Optimizat…

• AVI-Edit: Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner

  Haojie Zheng, Shuchen Weng, Jingqi Liu, Siqi Yang, Boxin Shi, Xinlong Wang · 12 de diciembre de 2025

  Recent advancements in video generation highlight that realistic audio-visual synchronization is crucial for engaging content creation. However, existing video editing methods largely overlook audio-visual synchronization and lack the fine-grained spatial and temporal controllability required for pr…

• IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web

  Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li · 4 de diciembre de 2025

  Recently advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of the robust benchmark specifically for assessing the Image-to-Web conversion proficiency of these large models. Primarily, it is essenti…

• StreamDiT: Real-Time Streaming Text-to-Video Generation

  Akio Kodaira, Tingbo Hou, Ji Hou, Markos Georgopoulos, Felix Juefei-Xu, Masayoshi Tomizuka, Yue Zhao · 17 de noviembre de 2025

  Recently, great progress has been achieved in text-to-video (T2V) generation by scaling transformer-based diffusion models to billions of parameters, which can generate high-quality videos. However, existing models typically produce only short clips offline, restricting their use cases in interactiv…

• Reliable Evaluation and Benchmarks for Statement Autoformalization

  Auguste Poiroux, Gail Weiss, Viktor Kun\v{c}ak, Antoine Bosselut · 30 de octubre de 2025

  Evaluating statement autoformalization, translating natural language mathematics into formal languages like Lean 4, remains a significant challenge, with few metrics, datasets, and standards to robustly measure progress. In this work, we present a comprehensive approach combining improved metrics, r…