Papers Human-Domain Subject-to-Video
“Human-Domain Subject-to-Video” 태그가 달린 논문 8편 · 필터 해제
MAGREF: Masked Guidance for Any-Reference Video Generation
Video generation has made substantial strides with the emergence of deep generative models, especially diffusion-based approaches. However, video generation based on multiple reference subjects still faces significant ch…
Human-Domain Subject-to-VideoOpen-Domain Subject-to-VideoSingle-Domain Subject-to-VideoVideo GenerationOpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
Subject-to-Video (S2V) generation aims to create videos that faithfully incorporate reference content, providing enhanced flexibility in the production of videos. To establish the infrastructure for S2V generation, we pr…
Human-Domain Subject-to-VideoOpen-Domain Subject-to-VideoSingle-Domain Subject-to-VideoVideo GenerationHunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation
Customized video generation aims to produce videos featuring specific subjects under flexible user-defined conditions, yet existing methods often struggle with identity consistency and limited input modalities. In this p…
Human-Domain Subject-to-VideoSingle-Domain Subject-to-VideoVideo AlignmentVideo GenerationSkyReels-A2: Compose Anything in Video Diffusion Transformers
This paper presents SkyReels-A2, a controllable video generation framework capable of assembling arbitrary visual elements (e.g., characters, objects, backgrounds) into synthesized videos based on textual prompts while m…
Human-Domain Subject-to-VideoOpen-Domain Subject-to-VideoSingle-Domain Subject-to-VideoVideo GenerationConcat-ID: Towards Universal Identity-Preserving Video Synthesis
We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs Variational Autoencoders to extract image features, which are concatenated with video latents along the sequence dimen…
Human-Domain Subject-to-VideoVideo GenerationVirtual Try-onVACE: All-in-One Video Creation and Editing
Diffusion Transformer has demonstrated powerful capability and scalability in generating high-quality images and videos. Further pursuing the unification of generation and editing tasks has yielded significant progress i…
AllHuman-Domain Subject-to-VideoOpen-Domain Subject-to-VideoSingle-Domain Subject-to-Video+2Phantom: Subject-consistent video generation via cross-modal alignment
The continuous development of foundational models for video generation is evolving into various applications, with subject-consistent video generation still in the exploratory stage. We refer to this as Subject-to-Video,…
cross-modal alignmentHuman-Domain Subject-to-VideoOpen-Domain Subject-to-VideoSingle-Domain Subject-to-Video+2Identity-Preserving Text-to-Video Generation by Frequency Decomposition
Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity. It is an important task in video generation but remains an open problem for generative models. This…
Human-Domain Subject-to-VideoImage to Video GenerationOpen-Domain Subject-to-VideoText-to-Video Generation+1