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Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer

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arxiv 2412.00733 v4 pith:DF6UGGHY submitted 2024-12-01 cs.CV cs.GRcs.LG

Jiahao Cui , Hui Li , Yun Zhan , Hanlin Shang , Kaihui Cheng , Yuqi Ma , Shan Mu , Hang Zhou
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Jingdong Wang Siyu Zhu
This is my paper
classification cs.CVcs.GRcs.LG
keywords videodynamicportraitrealisticaudioidentityanimationchallenges
verification ladder T0 review T1 audit T2 compute T3 formal
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Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we introduce the first application of a pretrained transformer-based video generative model that demonstrates strong generalization capabilities and generates highly dynamic, realistic videos for portrait animation, effectively addressing these challenges. The adoption of a new video backbone model makes previous U-Net-based methods for identity maintenance, audio conditioning, and video extrapolation inapplicable. To address this limitation, we design an identity reference network consisting of a causal 3D VAE combined with a stacked series of transformer layers, ensuring consistent facial identity across video sequences. Additionally, we investigate various speech audio conditioning and motion frame mechanisms to enable the generation of continuous video driven by speech audio. Our method is validated through experiments on benchmark and newly proposed wild datasets, demonstrating substantial improvements over prior methods in generating realistic portraits characterized by diverse orientations within dynamic and immersive scenes. Further visualizations and the source code are available at: https://fudan-generative-vision.github.io/hallo3/.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SyncBreaker:Stage-Aware Multimodal Adversarial Attacks on Audio-Driven Talking Head Generation

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    SyncBreaker jointly attacks image and audio streams with Multi-Interval Sampling and Cross-Attention Fooling to degrade speech-driven talking head generation more than single-modality baselines.

  2. InfinityHuman: Towards Long-Term Audio-Driven Human

    cs.CV 2025-08 conditional novelty 5.0 of 10

    A coarse-to-fine audio-driven animation framework that uses pose-guided refinement and hand-specific reward learning to generate long, identity-stable talking videos.

  3. MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation

    cs.CV 2025-08 reject novelty 5.0 of 10

    A new autoregressive video-generation framework for interactive digital humans, with a 64x compression autoencoder and a diffusion renderer, claims real-time multimodal control but is only demonstrated for audio input.

  4. InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing

    cs.CV 2025-08 conditional novelty 5.0 of 10

    Sparse-frame dubbing with adjacent-chunk keyframe sampling lets a streaming audio-video model produce full-body motion synchronized to new audio while preserving identity and camera motion.

  5. EDTalk++: Full Disentanglement for Controllable Talking Head Synthesis

    cs.CV 2025-08 conditional novelty 5.0 of 10

    EDTalk++ disentangles talking-head video into four orthogonal motion banks (mouth, pose, eyes, expression) and drives them from either video or audio inputs.

  6. MoDA: Multi-modal Diffusion Architecture for Talking Head Generation

    cs.GR 2025-07 conditional novelty 5.0 of 10

    MoDA uses flow matching in a compact face-motion space with a progressively fused multi-modal transformer to generate expressive, lip-synced talking-head videos from a single image and audio.

  7. FixTalk: Taming Identity Leakage for High-Quality Talking Head Generation in Extreme Cases

    cs.CV 2025-07 conditional novelty 5.0 of 10

    FixTalk adds two modules to a real-time GAN talking-head model, decoupling identity from motion to stop identity leakage while using a memory to recover details and reduce artifacts.

  8. Seeing Voices: Generating A-Roll Video from Audio with Mirage

    cs.CV 2025-06 reject novelty 5.0 of 10

    Mirage generates photorealistic A-roll videos of people speaking directly from audio, using only joint self-attention over audio, text, and video tokens.

  9. Preview WB-DH: Towards Whole Body Digital Human Bench for the Generation of Whole-body Talking Avatar Videos

    cs.CV 2025-08 reject novelty 4.0 of 10

    The paper previews a claimed 2M-clip multimodal benchmark for whole-body talking avatar video generation, with standard metrics and an initial evaluation of eight open-source models.

  10. JWB-DH-V1: Benchmark for Joint Whole-Body Talking Avatar and Speech Generation Version 1

    cs.CV 2025-07 reject novelty 4.0 of 10

    A paper announcing a large-scale whole-body talking avatar benchmark and evaluation protocol, but with insufficient details to verify the dataset or the joint audio-video evaluation.

  11. Livatar-1: Real-Time Talking Heads Generation with Tailored Flow Matching

    cs.CV 2025-07 reject novelty 4.0 of 10

    Livatar claims the best lip-sync score in its comparison table and real-time throughput, but the preprint does not describe the model or release any code, data, or weights.

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