LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis

• URL: http://arxiv.org/abs/2411.16748v1
• Date: Sun, 24 Nov 2024 04:46:00 GMT
• Title: LetsTalk: Latent Diffusion Transformer for Talking Video Synthesis
• Authors: Haojie Zhang, Zhihao Liang, Ruibo Fu, Zhengqi Wen, Xuefei Liu, Chenxing Li, Jianhua Tao, Yaling Liang,
• Abstract summary: LetsTalk is a diffusion transformer that incorporates modular temporal and spatial attention mechanisms to merge multimodality and enhance spatial-temporal consistency. We propose a suitable solution according to the modality differences of image, audio, and video generation. Our experiments demonstrate that our approach generates temporally coherent and realistic videos with enhanced diversity and liveliness.
• Score: 38.08071302910815
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Portrait image animation using audio has rapidly advanced, enabling the creation of increasingly realistic and expressive animated faces. The challenges of this multimodality-guided video generation task involve fusing various modalities while ensuring consistency in timing and portrait. We further seek to produce vivid talking heads. To address these challenges, we present LetsTalk (LatEnt Diffusion TranSformer for Talking Video Synthesis), a diffusion transformer that incorporates modular temporal and spatial attention mechanisms to merge multimodality and enhance spatial-temporal consistency. To handle multimodal conditions, we first summarize three fusion schemes, ranging from shallow to deep fusion compactness, and thoroughly explore their impact and applicability. Then we propose a suitable solution according to the modality differences of image, audio, and video generation. For portrait, we utilize a deep fusion scheme (Symbiotic Fusion) to ensure portrait consistency. For audio, we implement a shallow fusion scheme (Direct Fusion) to achieve audio-animation alignment while preserving diversity. Our extensive experiments demonstrate that our approach generates temporally coherent and realistic videos with enhanced diversity and liveliness.

Related papers

• AlignDiT: Multimodal Aligned Diffusion Transformer for Synchronized Speech Generation [41.74261260212531]
  multimodal-to-speech task has gained increasing attention due to its wide range of applications, such as film production, dubbing, and virtual avatars. Existing methods still suffer from limitations in speech intelligibility, audio-video synchronization, speech naturalness, and voice similarity to the reference speaker. We propose AlignDiT, a multimodal Aligned Diffusion Transformer that generates accurate, synchronized, and natural-sounding speech from aligned multimodal inputs.
  arXiv  Detail & Related papers  (2025-04-29T10:56:24Z)
• VideoFusion: A Spatio-Temporal Collaborative Network for Mutli-modal Video Fusion and Restoration [26.59510171451438]
  Existing multi-sensor fusion research predominantly integrates complementary from multiple images rather than videos. VideoFusion exploits cross-modal complementarity and temporal dynamics to generate context-temporally coherent videos. Extensive experiments reveal that VideoFusion outperforms existing image-oriented fusion paradigms in sequential scenarios.
  arXiv  Detail & Related papers  (2025-03-30T08:27:18Z)
• Cosh-DiT: Co-Speech Gesture Video Synthesis via Hybrid Audio-Visual Diffusion Transformers [58.86974149731874]
  Cosh-DiT is a Co-speech gesture video system with hybrid Diffusion Transformers. We introduce an audio Diffusion Transformer to synthesize expressive gesture dynamics synchronized with speech rhythms. For realistic video synthesis conditioned on the generated speech-driven motion, we design a visual Diffusion Transformer.
  arXiv  Detail & Related papers  (2025-03-13T01:36:05Z)
• High-fidelity and Lip-synced Talking Face Synthesis via Landmark-based Diffusion Model [89.29655924125461]
  We propose a novel landmark-based diffusion model for talking face generation. We first establish the less ambiguous mapping from audio to landmark motion of lip and jaw. Then, we introduce an innovative conditioning module called TalkFormer to align the synthesized motion with the motion represented by landmarks.
  arXiv  Detail & Related papers  (2024-08-10T02:58:28Z)
• Anchored Diffusion for Video Face Reenactment [17.343307538702238]
  We introduce Anchored Diffusion, a novel method for synthesizing relatively long and seamless videos. We train our model on video sequences with random non-uniform temporal spacing and incorporate temporal information via external guidance. During inference, we leverage the transformer architecture to modify the diffusion process, generating a batch of non-uniform sequences anchored to a common frame.
  arXiv  Detail & Related papers  (2024-07-21T13:14:17Z)
• MultiDiff: Consistent Novel View Synthesis from a Single Image [60.04215655745264]
  MultiDiff is a novel approach for consistent novel view synthesis of scenes from a single RGB image. Our results demonstrate that MultiDiff outperforms state-of-the-art methods on the challenging, real-world datasets RealEstate10K and ScanNet.
  arXiv  Detail & Related papers  (2024-06-26T17:53:51Z)
• Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image Animation [29.87407471246318]
  This research delves into the complexities of synchronizing facial movements and creating visually appealing, temporally consistent animations. Our innovative approach embraces the end-to-end diffusion paradigm and introduces a hierarchical audio-driven visual synthesis module. The proposed hierarchical audio-driven visual synthesis offers adaptive control over expression and pose diversity, enabling more effective personalization tailored to different identities.
  arXiv  Detail & Related papers  (2024-06-13T04:33:20Z)
• FaceChain-ImagineID: Freely Crafting High-Fidelity Diverse Talking Faces from Disentangled Audio [45.71036380866305]
  We abstract the process of people hearing speech, extracting meaningful cues, and creating dynamically audio-consistent talking faces from a single audio. Specifically, it involves two critical challenges: one is to effectively decouple identity, content, and emotion from entangled audio, and the other is to maintain intra-video diversity and inter-video consistency. We introduce the Controllable Coherent Frame generation, which involves the flexible integration of three trainable adapters with frozen Latent Diffusion Models.
  arXiv  Detail & Related papers  (2024-03-04T09:59:48Z)
• DiffSpeaker: Speech-Driven 3D Facial Animation with Diffusion Transformer [110.32147183360843]
  Speech-driven 3D facial animation is important for many multimedia applications. Recent work has shown promise in using either Diffusion models or Transformer architectures for this task. We present DiffSpeaker, a Transformer-based network equipped with novel biased conditional attention modules.
  arXiv  Detail & Related papers  (2024-02-08T14:39:16Z)
• Lumiere: A Space-Time Diffusion Model for Video Generation [75.54967294846686]
  We introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once. This is in contrast to existing video models which synthesize distants followed by temporal super-resolution. By deploying both spatial and (importantly) temporal down- and up-sampling, our model learns to directly generate a full-frame-rate, low-resolution video.
  arXiv  Detail & Related papers  (2024-01-23T18:05:25Z)
• SAiD: Speech-driven Blendshape Facial Animation with Diffusion [6.4271091365094515]
  Speech-driven 3D facial animation is challenging due to the scarcity of large-scale visual-audio datasets. We propose a speech-driven 3D facial animation with a diffusion model (SAiD), a lightweight Transformer-based U-Net with a cross-modality alignment bias between audio and visual to enhance lip synchronization.
  arXiv  Detail & Related papers  (2023-12-25T04:40:32Z)
• Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation [41.292644854306594]
  We propose a novel diffusion-based framework, named Diffusion Co-Speech Gesture (DiffGesture) DiffGesture achieves state-of-theart performance, which renders coherent gestures with better mode coverage and stronger audio correlations.
  arXiv  Detail & Related papers  (2023-03-16T07:32:31Z)
• Multimodal Token Fusion for Vision Transformers [54.81107795090239]
  We propose a multimodal token fusion method (TokenFusion) for transformer-based vision tasks. To effectively fuse multiple modalities, TokenFusion dynamically detects uninformative tokens and substitutes these tokens with projected and aggregated inter-modal features. The design of TokenFusion allows the transformer to learn correlations among multimodal features, while the single-modal transformer architecture remains largely intact.
  arXiv  Detail & Related papers  (2022-04-19T07:47:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.