Pith. sign in

REVIEW 4 major objections 5 minor 16 cited by

MIDAS claims that a standard language model, fed with audio, pose, and text tokens, can predict compressed video frames causally and render them with a diffusion head, making interactive digital humans real-time and multimodal.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

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.

T0 review reviewed 2026-08-05 challenge →

load-bearing objection System-design paper with a coherent recipe but the headline claims (multimodal control, real-time latency) outrun what the experiments actually show, so it reads as an early technical report rather than a substantiated system paper. the 4 major comments →

arxiv 2508.19320 v2 pith:LBOFZD3P submitted 2025-08-26 cs.CV cs.AI

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

Ming Chen , Liyuan Cui , Wenyuan Zhang , Haoxian Zhang , Yan Zhou , Xiaohan Li , Songlin Tang , Jiwen Liu
show 4 more authors
Borui Liao Hejia Chen Xiaoqiang Liu Pengfei Wan
This is my paper
classification cs.CV cs.AI
keywords digital humanautoregressive video generationmultimodal controlreal-time interactionaudio-driven talking facedeep compression autoencoderflow matchinglarge language model
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

MIDAS sets out to prove that real-time interactive digital humans do not need a bespoke video-generation architecture: a standard autoregressive language model, lightly adapted, can predict the next chunk of compressed video from audio, pose, and text conditions, while a diffusion head renders each frame. The payoff is a system that can carry on a duplex conversation, switch languages mid-song, and even act as an interactive world model, all without pre-specifying the full control sequence. To make this tractable, the paper introduces a 64x-spatial-compression autoencoder that shrinks each frame to 60 tokens, and a 20,000-hour dialogue dataset. If the claims hold, interactive avatar video stops being a two-stage offline pipeline and becomes a streaming token-prediction problem.

Core claim

The paper's central claim is that interactive digital-human synthesis can be reframed as causal next-frame latent prediction in a standard LLM. Modality encoders turn audio, pose, and text into condition tokens; a reference image and six target frames are converted into compact latent tokens by a deep-compression autoencoder with 64x spatial reduction and 60 tokens per frame. The LLM predicts the next frame's tokens from the condition tokens plus history, using a chunked causal attention mask, and a lightweight diffusion-transformer head denoises those tokens into frames in four steps. Training adds controlled Gaussian corruption to context frames so the model learns to recover from its own

What carries the argument

The load-bearing object is the chunked, causal token sequence: in each 480ms chunk, condition tokens (audio, pose, text) precede the reference-image token and the target frame tokens, and a frame-level causal attention mask lets each frame attend only to conditions, its own intra-frame tokens, and earlier frames. This makes the standard LLM a streaming next-frame predictor. The second pillar is the Deep Compression Autoencoder (DC-AE), a 64x-spatial-compression VAE with causal temporal 3D convolutions and rotary-position attention, streamed frame-by-frame with a 5-frame history cache; it is what makes long-horizon autoregression affordable. The diffusion head is the third pillar: it renders

Load-bearing premise

The entire system assumes that the causal streaming 64x-compression autoencoder, using only a 5-frame history, retains enough facial identity and visual fidelity for high-quality synthesis, yet the paper reports no PSNR/SSIM or comparable reconstruction measurements.

What would settle it

Run a live duplex session and simultaneously log two numbers: wall-clock time per 480ms chunk, and DC-AE reconstruction PSNR/SSIM on held-out portraits. Real-time interaction fails if the first exceeds 480ms; long-horizon identity coherence fails if the second is below the level of standard talking-face autoencoders.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Real-time duplex conversation works: avatars take turns and display listening behavior, with controls streamed in 480ms chunks rather than fixed for the whole video.
  • Long videos stay coherent: the noise-injection scheme plus 8-chunk truncation keeps generation stable for up to an hour of a single identity and four-minute multilingual clips without significant drift.
  • Multimodal control is additive: audio, pose, and text are encoded into the same token stream, so combinations of controls can be swapped in without retraining the backbone.
  • The same architecture transfers to non-human domains: directional tokens trained on Minecraft data produce an interactive world model with visual consistency and memory.
  • Lower latency follows directly from compression: 64x spatial reduction cuts the number of video tokens the LLM must predict, which is what makes streaming feasible.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Because conditions are attached per chunk rather than fixed for the whole sequence, the same machinery should let a user interrupt or redirect an avatar mid-conversation, such as changing emotion, language, or subject, without restarting generation; the paper demonstrates chunk-level audio driving but not this redirect scenario explicitly.
  • The 64x compression is the real hidden bottleneck: if reconstruction fidelity at that ratio degrades identity, no amount of autoregressive modeling or denoising can restore it. Reporting PSNR/SSIM for the DC-AE would directly bound how far the pipeline can scale.
  • The framework's claim to be a 'world model' is a natural extension of the same token stream: any signal that can be encoded as tokens, such as gaze, scene layout, or game-controller input, can in principle steer the predictor, making the method a candidate backbone for controllable simulators beyond human avatars.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes MIDAS, an autoregressive video generation framework for interactive digital humans. It claims to accept multimodal conditions (audio, pose, text), to operate in real time via streaming next-frame prediction, and to achieve long-horizon stability via a 64× compressed latent space and a diffusion head. The method combines a Qwen2.5-3B LLM backbone, a custom Deep Compression Autoencoder with causal temporal modules, a noise-injection exposure-bias mitigation scheme, and a ~20,000-hour dialogue dataset. Experiments are qualitative: duplex conversation, multilingual singing, and a Minecraft-based interactive world model. The paper does not report quantitative evaluations.

Significance. If the claims were substantiated, the work would be significant: a unified LLM-based autoregressive architecture for streaming multimodal digital-human synthesis with real-time latency would address a practically important gap. The design choices (LLM backbone with minimal modification, flow-matching diffusion head, deep compression VAE) are sensible and the dataset construction is a practical contribution. However, the reported evidence is entirely qualitative; core quantitative claims—latency, reconstruction fidelity, identity preservation, and multimodal controllability—are not measured. The paper should be credited for being explicit in Section 4.2 that pose and text conditioning are future work, and for providing the architecture details necessary to reproduce the pipeline, but the absence of metrics leaves the central claims unverified.

major comments (4)
  1. [Abstract/§1/§4.2] The abstract and introduction claim 'interactive multimodal control' including pose and text, but Section 4.2 explicitly states 'we leave pose and text conditioning as future work.' Only audio-driven results are shown. The multimodal-control claim is therefore unsupported by the experiments, and the headline capability is not demonstrated.
  2. [§3.3, §3.5, §4.1] The paper repeatedly asserts 'real-time,' 'low-latency,' and 'streamable' operation, yet no measured latency, throughput, FPS, or end-to-end delay is reported. With a 3B LLM, a 0.5B diffusion head, 60 tokens/frame, and 4 denoising steps, real-time operation is non-obvious; a runtime benchmark is essential to the paper's central claim.
  3. [§3.2.1 and §4] The Deep Compression Autoencoder with 64× spatial compression and a 5-frame causal history is load-bearing: it determines token count, reconstruction quality, and temporal consistency. No reconstruction metrics (PSNR/SSIM/LPIPS), identity-similarity scores, or ablations of the 5-frame window are provided. The entire qualitative pipeline rests on this unmeasured premise.
  4. [§3.5/§4.2] The claim of 'up to one hour' and '4 minutes without significant drift' is stated without a protocol, a definition of drift, or associated quantitative evidence. A qualitative still-frame figure does not support a temporal-stability claim of this strength.
minor comments (5)
  1. [§3.4] Equation (2) defines x_noisy = σ·ε + (1−σ)·x with 'σ' as noise level, but 'σ' conventionally denotes standard deviation; the text should clarify the corruption schedule and that the same noise level is sampled per frame.
  2. [§3.2.1] The description of the DC-AE training is terse: '8-frame temporal window,' 'asymmetric replicate padding,' 'RoPE-based attention layers' are mentioned without details. Add a table or precise layer configuration.
  3. [§3.1] The dataset is a major contribution claim, but no statistics are given for the number of identities, language distribution, or hours per source; please provide a dataset table.
  4. [References] Reference [11] is cited as both OmniHuman-1 (a) and (b) with identical content but different years; unify and correct.
  5. [§4.1] Report the exact number of tokens per frame and the spatial latent resolution for 384×640 input, since 'up to 60 tokens' is ambiguous.

Circularity Check

0 steps flagged

No significant circularity found: the derivation chain is self-contained and no prediction reduces to an input by construction.

full rationale

I walked the paper's claimed derivation chain and found no circular step that reduces a prediction to an input by construction. The central components are architectural designs (LLM backbone, DC-AE, diffusion head, noise injection, chunked streaming) and empirical claims about training a system on a constructed dataset; no equation is defined in terms of a quantity it is supposed to predict, and no fitted parameter is relabeled as a prediction. The only overlapping-author citation is LivePortrait (Guo et al., 2024), which appears in Related Work as a GAN-based baseline and is not load-bearing for any MIDAS design choice, uniqueness claim, or derived result. The paper's own Section 4.2 explicitly states that pose and text conditioning are left to future work, and no wall-clock latency or throughput numbers are provided; these are gaps between the abstract's 'real-time multimodal control' headline and the reported experiments, but they are evidentiary weaknesses, not circularity. Similarly, the DC-AE 64x compression is asserted without reconstruction metrics, which is an unmeasured premise rather than a circular definition. I therefore assign score 0: the derivation, such as it is, does not import its conclusions from its inputs.

Axiom & Free-Parameter Ledger

7 free parameters · 5 axioms · 0 invented entities

No new particles, forces, or entities. The DC-AE and noise-injection module are engineering components, not postulated entities. The main explanatory burden falls on unmeasured design parameters and assumptions.

free parameters (7)
  • Maximum noise scale for exposure-bias injection (sigma=0.5) = 0.5
    Hand-chosen; the paper gives no ablation across noise scales.
  • Number of noise-level buckets = 20
    Discretization of noise levels; no analysis of sensitivity.
  • Chunk size for streaming = 6 frames / 480 ms
    Used to balance latency and context; no ablation reported.
  • Causal streaming history length for DC-AE = 5 frames
    Chosen as a practical trade-off between efficiency and quality; paper states longer histories may be better.
  • Context truncation window = 8 chunks
    Inspired by FAR; number chosen without experimental justification.
  • Number of denoising steps = 4
    Chosen for efficiency/quality balance; no comparison to other step counts.
  • Spatial compression ratio and channels of DC-AE = 64x, 128 channels
    Core design claim; no reconstruction quality metrics provided.
axioms (5)
  • domain assumption Causal streaming encoding/decoding with a 5-frame history and cached features maintains temporal consistency.
    Section 3.2.1 asserts this trade-off without comparison to longer histories.
  • domain assumption Noise injection up to sigma=0.5 with 20 buckets sufficiently closes the train/inference gap.
    Section 3.4 states this bridges the domain gap; no ablation.
  • domain assumption Flow matching with 4 denoising steps yields high-quality frames from AR hidden states.
    Section 4.1 chooses 4 steps as effective balance; no quality comparison.
  • domain assumption The curated 20,000-hour dataset provides sufficient coverage and balance for general dialogue scenarios.
    Section 3.1 describes filtering but no dataset statistics or downstream analysis are reported.
  • ad hoc to paper The multimodal condition projector generalizes to pose and text even though trained/tested only with audio in experiments.
    Section 3.2.2 defines pose/text encoders; Section 4.2 defers them to future work, so the generalization is assumed.

reviewed 2026-08-05 · how reviews work

0 comments
Cite this review

Pith. "Pith review of MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation." pith.science (2026). https://pith.science/paper/LBOFZD3P

@misc{pith2026250819320,
  author       = {Pith},
  title        = {Pith review of: MIDAS: Multimodal Interactive Digital-humAn Synthesis via Real-time Autoregressive Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LBOFZD3P}},
  note         = {Machine review of arXiv:2508.19320}
}
Share X Bluesky LinkedIn Reddit HN
abstract

Recently, interactive digital human video generation has attracted widespread attention and achieved remarkable progress. However, building such a practical system that can interact with diverse input signals in real time remains challenging to existing methods, which often struggle with heavy computational cost and limited controllability. In this work, we introduce an autoregressive video generation framework that enables interactive multimodal control and low-latency extrapolation in a streaming manner. With minimal modifications to a standard large language model (LLM), our framework accepts multimodal condition encodings including audio, pose, and text, and outputs spatially and semantically coherent representations to guide the denoising process of a diffusion head. To support this, we construct a large-scale dialogue dataset of approximately 20,000 hours from multiple sources, providing rich conversational scenarios for training. We further introduce a deep compression autoencoder with up to 64$\times$ reduction ratio, which effectively alleviates the long-horizon inference burden of the autoregressive model. Extensive experiments on duplex conversation, multilingual human synthesis, and interactive world model highlight the advantages of our approach in low latency, high efficiency, and fine-grained multimodal controllability.

Figures

Figures reproduced from arXiv: 2508.19320 by Borui Liao, Haoxian Zhang, Hejia Chen, Jiwen Liu, Liyuan Cui, Ming Chen, Pengfei Wan, Songlin Tang, Wenyuan Zhang, Xiaohan Li, Xiaoqiang Liu, Yan Zhou.

Figure 1
Figure 1. Figure 1: Overview of the dataset pipeline. The process consists of three main stages: (1) Data collection and pre-processing; (2) Annotation and data construction; (3) Post-processing. Temporal Module Temporal Module Decoder Block Temporal Module Temporal Module Temporal Module Temporal Module ...... ...... ...... ...... ...... Temporal Features Cache Encoder Block x N Downsample Block Space to Channel Decoder Bloc… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of our Deep Compression Autoencoder (DC-AE). We first train the DC￾AE with a spatial compression ratio of 64. In the second phase, we perform causal temporal module training. Then we apply full-model fine-tuning using an 8-frame temporal window in the third phase. Post-processing. The annotated data undergoes manual review combined with automatic sampling to ensure balanced and high-quality su… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our model architecture. Our streaming generation framework processes inputs in chunks, where each chunk contains condition tokens (audio, pose, text) followed by frame tokens. We apply teacher forcing during training, while performing next-frame prediction during inference. We corrupt frame tokens with Gaussian noise to mitigate exposure bias. The AR output serves as guidance for the diffusion … view at source ↗
Figure 4
Figure 4. Figure 4: Illustration of our causal attention mask [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Multi-speaker digital conversation with audio-driven avatars. Speaker 1 (top) and Speaker 2 (bottom) demonstrating turn-taking dialogue, with corresponding audio waveforms shown in blue and green. For long-duration generation, we introduce a specialized post-training phase using identity-specific data at higher resolution and frame rates (12.5 fps → 25fps). Unlike prior approaches that rely on vast amounts… view at source ↗
Figure 6
Figure 6. Figure 6: Cross-lingual singing generation with synchronized lip movements. Our model accu￾rately renders lip synchronization across multiple languages, demonstrating the model’s fine-grained understanding of cross-lingual phoneme-to-viseme mapping without explicit language identifica￾tion. 10−5 for both the main model and pretraining components, combined with a cosine learning rate scheduler, 1000 warmup steps, and… view at source ↗
Figure 7
Figure 7. Figure 7: General controllable video generation on Minecraft dataset. By incorporating di￾rectional control into our multimodal condition encoding, we realize a real-time interactive world model that exhibits remarkable visual consistency and memory capabilities. Multi-language Long Video Synthesis. We present an example of cross-lingual singing synthesis in [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 16 Pith papers

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

  1. InstructAV2AV: Instruction-Guided Audio-Video Joint Editing

    cs.CV 2026-05 unverdicted novelty 7.0

    InstructAV2AV is an end-to-end instruction-guided audio-video joint editing model that adapts a pre-trained backbone with gated attention and two-stage training, outperforming prior methods on 11 metrics after buildin...

  2. AsymTalker: Identity-Consistent Long-Term Talking Head Generation via Asymmetric Distillation

    cs.LG 2026-05 unverdicted novelty 7.0

    AsymTalker maintains identity consistency in long-term diffusion talking-head videos by encoding temporal references from a static image and training a student model under inference-like conditions via asymmetric dist...

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

    cs.CV 2025-12 unverdicted novelty 7.0

    AVI-Edit enables precise audio-synchronized instance-level video editing via a granularity-aware mask refiner, a self-feedback audio agent, and a new large-scale annotated dataset.

  4. EchoCache: Energy-Guided Cross-Modal Caching for Efficient Audio-Driven Video Generation

    cs.CV 2026-08 conditional novelty 6.0

    Audio time-frequency energy guides which video latents get recomputed during diffusion denoising, yielding up to 2.46x faster audio-driven video generation with competitive quality.

  5. OmniMate: Open-Ended Real-Time Streaming Audio-Visual Generation for Interactive Avatars

    cs.CV 2026-07 conditional novelty 6.0

    OmniMate generates real-time streaming video, speech, and sound effects for interactive avatars while preserving the same face and voice across long multi-turn conversations.

  6. Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models

    cs.CV 2026-06 unverdicted novelty 6.0

    Wan-Streamer is a unified end-to-end Transformer for low-latency streaming audio-visual interaction using block-causal attention on interleaved multimodal tokens.

  7. SocialDirector: Training-Free Social Interaction Control for Multi-Person Video Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    SocialDirector uses spatiotemporal actor masking and directional reweighting on cross-attention maps to reduce actor-action mismatches and improve target-directed interactions in generated multi-person videos.

  8. AsymTalker: Identity-Consistent Long-Term Talking Head Generation via Asymmetric Distillation

    cs.LG 2026-05 unverdicted novelty 6.0

    AsymK-Talker introduces kernel-conditioned loop generation, temporal reference encoding, and asymmetric kernel distillation to achieve real-time, drift-resistant talking head synthesis from audio using diffusion models.

  9. AsymTalker: Identity-Consistent Long-Term Talking Head Generation via Asymmetric Distillation

    cs.LG 2026-05 unverdicted novelty 6.0

    AsymTalker uses temporal reference encoding and asymmetric knowledge distillation to produce identity-consistent talking head videos up to 600 seconds long at 66 FPS.

  10. Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

    cs.CV 2025-12 conditional novelty 6.0

    Live Avatar reports real-time streamable generation from a 14B audio-driven diffusion model at ~20 FPS on 5 H800s with stable identity over 10,000 seconds.

  11. Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

    cs.CV 2025-12 conditional novelty 6.0

    Live Avatar enables 45 FPS real-time streaming infinite-length audio-driven avatar generation from a 14B diffusion model via distillation and timestep-forcing pipeline parallelism.

  12. MindFlow: Harmonizing Cognitive Semantics and Acoustic Dynamics for Facial Animation Generation in Dyadic Conversations

    cs.CV 2026-06 unverdicted novelty 5.0

    MindFlow presents a neuroscience-inspired dual-stream generative model that uses chunk-state emotional modeling and conditional flow matching to produce facial animations with improved semantic fit and motion realism ...

  13. Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models

    cs.CV 2026-06 unverdicted novelty 5.0

    Wan-Streamer presents a unified end-to-end Transformer for low-latency multimodal streaming interaction without external modules.

  14. Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models

    cs.CV 2026-06 unverdicted novelty 5.0

    Wan-Streamer is a unified Transformer model for low-latency streaming audio-visual interaction that jointly handles perception, reasoning, generation, and timing without external modules.

  15. Multimodal Large Language Model-Enabled Video Translation: A Role-Oriented Survey

    cs.CV 2026-04 accept novelty 5.0

    MLLM-enabled video translation is usefully framed as three roles—Semantic Reasoner, Expressive Performer, and Visual Synthesizer—rather than a cascade of ASR, MT, TTS, and lip-sync.

  16. Multimodal Large Language Model-Enabled Video Translation: A Role-Oriented Survey

    cs.CV 2026-04 unverdicted novelty 4.0

    The paper offers the first focused review of MLLM-based video translation organized by a three-role taxonomy of Semantic Reasoner, Expressive Performer, and Visual Synthesizer, plus open challenges.

Reference graph

Works this paper leans on

19 extracted references · 2 canonical work pages · cited by 10 Pith papers

  1. [1]

    Lrs3-ted: a large-scale dataset for visual speech recognition

    Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Lrs3-ted: a large-scale dataset for visual speech recognition. arXiv preprint arXiv:1809.00496,

    Pith/arXiv arXiv

  2. [4]

    Hallo3: Highly dynamic and realistic portrait image animation with video diffusion transformer

    Jiahao Cui, Hui Li, Yun Zhan, Hanlin Shang, Kaihui Cheng, Yuqi Ma, Shan Mu, Hang Zhou, Jingdong Wang, and Siyu Zhu. Hallo3: Highly dynamic and realistic portrait image animation with video diffusion transformer. arXiv preprint arXiv:2412.00733,

    Pith/arXiv arXiv

  3. [6]

    Liveportrait: Efficient portrait animation with stitching and retargeting control

    Jianzhu Guo, Dingyun Zhang, Xiaoqiang Liu, Zhizhou Zhong, Yuan Zhang, Pengfei Wan, and Di Zhang. Liveportrait: Efficient portrait animation with stitching and retargeting control. arXiv preprint arXiv:2407.03168,

    Pith/arXiv arXiv

  4. [8]

    Loopy: Taming audio-driven portrait avatar with long-term motion dependency

    Jianwen Jiang, Chao Liang, Jiaqi Yang, Gaojie Lin, Tianyun Zhong, and Yanbo Zheng. Loopy: Taming audio-driven portrait avatar with long-term motion dependency. arXiv preprint arXiv:2409.02634,

    Pith/arXiv arXiv

  5. [9]

    Let them talk: Audio-driven multi-person conversational video generation

    Zhe Kong, Feng Gao, Yong Zhang, Zhuoliang Kang, Xiaoming Wei, Xunliang Cai, Guanying Chen, and Wenhan Luo. Let them talk: Audio-driven multi-person conversational video generation. arXiv preprint arXiv:2505.22647,

    Pith/arXiv arXiv

  6. [10]

    Omnihuman-1: Re- thinking the scaling-up of one-stage conditioned human animation models

    Gaojie Lin, Jianwen Jiang, Jiaqi Yang, Zerong Zheng, and Chao Liang. Omnihuman-1: Re- thinking the scaling-up of one-stage conditioned human animation models. arXiv preprint arXiv:2502.01061, 2025a. 11 Technical Report Gaojie Lin, Jianwen Jiang, Jiaqi Yang, Zerong Zheng, and Chao Liang. Omnihuman-1: Re- thinking the scaling-up of one-stage conditioned hum...

    Pith/arXiv arXiv

  7. [11]

    Talkingmachines: Real-time audio-driven facetime-style video via autoregressive diffusion models

    Chetwin Low and Weimin Wang. Talkingmachines: Real-time audio-driven facetime-style video via autoregressive diffusion models. arXiv preprint arXiv:2506.03099,

    Pith/arXiv arXiv

  8. [12]

    V oxceleb: A large-scale speaker iden- tification dataset

    Arsha Nagraniy, Joon Son Chungy, and Andrew Zisserman. V oxceleb: A large-scale speaker iden- tification dataset. In Proceedings of the Annual Conference of the International Speech Commu- nication Association, INTERSPEECH, volume 2017, pp. 2616–2620,

    work page 2017

  9. [14]

    org/abs/2212.04356

    URL https://arxiv. org/abs/2212.04356. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research, 21(140):1–67,

    Pith/arXiv arXiv

  10. [15]

    Magi-1: Autoregressive video generation at scale

    Hansi Teng, Hongyu Jia, Lei Sun, Lingzhi Li, Maolin Li, Mingqiu Tang, Shuai Han, Tianning Zhang, WQ Zhang, Weifeng Luo, et al. Magi-1: Autoregressive video generation at scale. arXiv preprint arXiv:2505.13211,

    Pith/arXiv arXiv

  11. [16]

    Emo2: End-effector guided audio- driven avatar video generation

    Linrui Tian, Siqi Hu, Qi Wang, Bang Zhang, and Liefeng Bo. Emo2: End-effector guided audio- driven avatar video generation. arXiv preprint arXiv:2501.10687,

    Pith/arXiv arXiv

  12. [17]

    Diffusion models are real-time game engines

    Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion models are real-time game engines. arXiv preprint arXiv:2408.14837,

    Pith/arXiv arXiv

  13. [2017]

    Chatanyone: Styl- ized real-time portrait video generation with hierarchical motion diffusion model

    Jinwei Qi, Chaonan Ji, Sheng Xu, Peng Zhang, Bang Zhang, and Liefeng Bo. Chatanyone: Styl- ized real-time portrait video generation with hierarchical motion diffusion model. arXiv preprint arXiv:2503.21144,

    Pith/arXiv arXiv

  14. [2018]

    Body of her: A preliminary study on end-to-end humanoid agent

    Tenglong Ao. Body of her: A preliminary study on end-to-end humanoid agent. arXiv preprint arXiv:2408.02879,

    Pith/arXiv arXiv

  15. [2021]

    Mocha: Towards movie-grade talking character synthesis

    12 Technical Report Cong Wei, Bo Sun, Haoyu Ma, Ji Hou, Felix Juefei-Xu, Zecheng He, Xiaoliang Dai, Luxin Zhang, Kunpeng Li, Tingbo Hou, et al. Mocha: Towards movie-grade talking character synthesis. arXiv preprint arXiv:2503.23307,

    Pith/arXiv arXiv

  16. [2022]

    Long-context autoregressive video modeling with next-frame prediction

    Yuchao Gu, Weijia Mao, and Mike Zheng Shou. Long-context autoregressive video modeling with next-frame prediction. arXiv preprint arXiv:2503.19325,

    Pith/arXiv arXiv

  17. [2023]

    Qwen2.5 technical report

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu X...

    Pith/arXiv arXiv

  18. [2024]

    Bind-your-avatar: Multi-talking-character video generation with dynamic 3d-mask-based embedding router

    Yubo Huang, Weiqiang Wang, Sirui Zhao, Tong Xu, Lin Liu, and Enhong Chen. Bind-your-avatar: Multi-talking-character video generation with dynamic 3d-mask-based embedding router. arXiv preprint arXiv:2506.19833,

    Pith/arXiv arXiv

  19. [2025]

    V oxceleb2: Deep speaker recognition

    Joon Son Chung, Arsha Nagrani, and Andrew Zisserman. V oxceleb2: Deep speaker recognition. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, volume 2018, pp. 1086–1090,

    work page 2018

This paper was first reviewed by deepseek-v4-flash on August 5, 2026.