1 citations · 1 across the 12 of their papers we have counts for
13 papers
SPVC: Structured and Panoptic Video Fixing for Cross-Dataset Driving Scene Rendering
Gen Li, Shu Han, Yun Xi Qiao +5
Driving scene reconstruction and rendering, especially with 3D Gaussian Splatting, has become an important component of autonomous driving simulation. However, rendered views often…
LibraGen: Playing a Balance Game in Subject-Driven Video Generation
Jiahao Zhu, Shanshan Lao, Lijie Liu +10
With the advancement of video generation foundation models (VGFMs), customized generation, particularly subject-to-video (S2V), has attracted growing attention. However, a key chal…
FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation
Gen Li, Peiyu Liu
Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but cu…
DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
Jiawei Liu, Junqiao Li, Jiangfan Deng +11
The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real…
Leveraging Overfitting for Low-Complexity and Modality-Agnostic Joint Source-Channel Coding
Haotian Wu, Gen Li, Pier Luigi Dragotti +1
This paper introduces Implicit-JSCC, a novel overfitted joint source-channel coding paradigm that directly optimizes channel symbols and a lightweight neural decoder for each sourc…
Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model
Team Seedance, Heyi Chen, Siyan Chen +194
Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically f…
DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images
Xiaoxue Chen, Ziyi Xiong, Yuantao Chen +11
Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimiz…
OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models
Jinshu Chen, Xinghui Li, Xu Bai +8
Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiti…
HuMo: Human-Centric Video Generation via Collaborative Multi-Modal Conditioning
Liyang Chen, Tianxiang Ma, Jiawei Liu +7
Human-Centric Video Generation (HCVG) methods seek to synthesize human videos from multimodal inputs, including text, image, and audio. Existing methods struggle to effectively coo…
MoDA: Multi-modal Diffusion Architecture for Talking Head Generation
Xinyang Li, Gen Li, Zhihui Lin +6
Talking head generation with arbitrary identities and speech audio remains a crucial problem in the realm of the virtual metaverse. Recently, diffusion models have become a popular…
Phantom-Data : Towards a General Subject-Consistent Video Generation Dataset
Zhuowei Chen, Bingchuan Li, Tianxiang Ma +8
Subject-to-video generation has witnessed substantial progress in recent years. However, existing models still face significant challenges in faithfully following textual instructi…
AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion
Mingzhen Sun, Weining Wang, Gen Li +7
The task of video generation requires synthesizing visually realistic and temporally coherent video frames. Existing methods primarily use asynchronous auto-regressive models or sy…
Deep Understanding of Soccer Match Videos
Shikun Xu, Yandong Zhu, Gen Li +1
Soccer is one of the most popular sport worldwide, with live broadcasts frequently available for major matches. However, extracting detailed, frame-by-frame information on player a…