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REVIEW 4 major objections 5 minor 38 references

A training-free keyframe-chaining pipeline preserves identity across sequential-action videos, ranking third on the IPVG26 Track 2 leaderboard.

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 →

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T0 review

2026-08-01 16:26 UTC pith:J2XVJSRA

load-bearing objection A competent challenge-system report with a genuine third-place result; the keyframe-chaining contribution is plausible but under-ablated. the 4 major comments →

arxiv 2607.17985 v1 pith:J2XVJSRA submitted 2026-07-20 cs.CV cs.MM

Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation

Zhenjie Liu , Binyan Chen , Hao Chen , Tong Pan , Shangfei Wang This is my paper
classification cs.CV cs.MM
keywords identity-preserving video generationsequential actionskeyframe generationterminal-state promptsdiffusion modelinference-time guidancenoise searchtemporal alignment
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.

This paper is trying to establish that identity-preserving video generation with a scripted sequence of timed actions can be solved without retraining a video model, by decomposing the task into a chain of keyframes anchored at the terminal state of each action. The core idea is to generate each keyframe from the reference image (the time-invariant identity anchor) and the previous keyframe (the time-varying pose cue), so appearance and motion are handled by separate conditioning signals. The intervening video segments are then produced by a keyframe-interpolation video model, with two training-free sampling-time tricks — multi-reference guidance and identity-driven noise search — added to reinforce identity. The paper reports a third-place finish on the official Track 2 leaderboard, with the largest gains on identity-preservation and temporal-alignment metrics.

Core claim

The central claim is that sequential-action identity preservation reduces to a keyframe-anchored decomposition. Instead of generating the whole clip from a single prompt, the pipeline first rewrites the global scene description and each timestamped action caption into image-generation prompts that specify the terminal state of each segment. A chained keyframe generator then produces one keyframe per segment boundary, conditioning each on the reference image jointly with the preceding keyframe, thereby separating time-invariant identity from time-varying pose. Finally, a video model in keyframe-interpolation mode fills in the segments, guided by a multi-reference guidance term that amplifies

What carries the argument

The load-bearing object is the chained keyframe sequence: keyframes are generated sequentially, with each keyframe conditioned on the same reference image (the persistent identity anchor) plus the immediately preceding keyframe (the spatial/pose continuity cue) and a prompt describing the terminal state of the action segment. This triple conditioning is what the paper credits with decoupling identity from motion. Two inference-time mechanisms reinforce it: multi-reference guidance adds an explicit modality-guidance term to the classifier-free guidance score that amplifies the boundary keyframes' visual influence, and identity-driven noise searching scores candidate initial noises by cosine s

Load-bearing premise

The pipeline assumes that conditioning each keyframe on the reference image and the immediately preceding keyframe is sufficient to keep the identity stable, i.e., that the base image model will carry the reference's identity through the chain without accumulating drift; a single identity error at an early keyframe propagates to every later keyframe.

What would settle it

Generate a chain of N keyframes for a facial subject and measure the face-embedding similarity between each keyframe and the reference image. If the similarity drops monotonically (or by more than a small tolerance) as N increases, or if a human evaluator can spot the subject changing across the chain, then the chaining premise fails even though the video model and guidance are unchanged. Alternatively, replace the predecessor keyframe K_{i-1} with a frame of a different person and check whether the output already deviates from the reference; if it does, the reference alone is not anchoring id

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

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If this is right

  • If the central claim holds, a training-free recipe of terminal-state prompts, chained keyframes, and interpolation is a viable alternative to fine-tuning for identity-preserving multi-action generation.
  • The same pipeline can be reassembled with any compatible off-the-shelf image model and keyframe-interpolation video model, so progress in those base models directly transfers.
  • Identity fidelity in video can be improved at sampling time (guidance and noise selection) without retraining, which is a cheaper lever than per-identity fine-tuning.
  • The terminal-state prompt rewriting technique turns timestamped action captions into well-posed image generation targets, which may be reusable for other temporal-to-image decompositions.

Where Pith is reading between the lines

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

  • The chained generation premise invites a direct stress test: measuring identity similarity at each keyframe would reveal whether errors accumulate along the chain; if they do, a bidirectional or global-conditioning variant (e.g., conditioning each keyframe on the reference plus the first and last anchors) could bound the drift.
  • Because the noise-search reward uses a face embedding, extending it to objects and animals would require a more general similarity function; a DINOv2-based reward, or a learned reward, could make the enhancement category-agnostic.
  • The terminal-state prompt idea suggests that other temporally structured tasks (e.g., multi-stage image editing, storyboard generation) could be reformulated as keyframe-anchored image generation.
  • The leaderboard result is a single-run outcome; a per-keyframe identity analysis and a study of how performance scales with the number of actions would clarify where the method's limits lie.

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 a training-free, three-stage pipeline for sequential-action identity-preserving video generation, targeting the IPVG26 Track 2 setting in which a subject must perform a timestamped sequence of actions while preserving identity. Stage 1 uses a multimodal LLM to rewrite the global prompt and per-segment captions into terminal-state image-generation prompts. Stage 2 generates a chained sequence of keyframes, each conditioned jointly on the reference identity image and the preceding keyframe, in order to decouple time-invariant appearance from time-varying pose. Stage 3 synthesizes each video segment from boundary keyframes using multi-reference guidance, identity-driven noise search, and a variable-length frame allocation strategy. The pipeline ranked third on the official Track 2 leaderboard. The paper reports a backbone comparison (LTX-2.3 vs. Wan2.1-VACE) and an ablation of the two Stage-3 enhancements. The central claim is that keyframe-anchored decomposition with terminal-state targeting is an effective recipe for timed multi-action identity-preserving video generation.

Significance. If the central claim holds, the paper provides a practical, zero-training recipe that combines off-the-shelf image and video generators with LLM-based prompt rewriting and inference-time identity guidance. The external leaderboard rank is a genuine strength, and the method is described in enough detail to be reproduced. However, the evidence as presented does not isolate the contribution of the keyframe chaining—the paper's principal methodological novelty—and the evaluation methodology (development and tuning on the same 200-sample official test set, no error bars or significance tests, and a noise-search reward that resembles the official identity metric) weakens the attribution of the reported gains. The ideas are plausible and worth publication after the identified gaps are addressed.

major comments (4)
  1. [§4.3, Table 3] The core contribution—chained keyframe generation (Section 3.3)—is never ablated. Table 1 compares two video backbones inside the identical chained-keyframe pipeline, and Table 3 removes only Multi-Reference Guidance and Noise Searching. No experiment removes or replaces the chaining itself (e.g., generating keyframes independently from I_ref only, or running single-pass video generation after prompt polishing). Therefore the leaderboard score and the overall comparison cannot be attributed to the keyframe-anchored design; the gains could come from prompt polishing alone or from the LTX-2.3 backbone. The paper needs a Stage-2 ablation to make the central claim load-bearing.
  2. [§4.1, Tables 1–3] All quantitative development—backbone choice, guidance weights (w_cfg, w_stg, w_mod), frame budget F_min, noise-search candidates K and lookahead steps T', and the ablations—was performed on the same official 200-sample test set, with no validation split, error bars, or significance tests. Since the official overall score is the ranking metric and was used for model selection, the small reported deltas (e.g., overall 0.4971 vs. 0.4910 vs. 0.4820) may reflect selection on the test set rather than robust improvements. The paper should provide confidence intervals, bootstrap estimates, or a held-out validation split to support the generalization claim.
  3. [§3.3, Fig. 3] The chaining premise—that conditioning each keyframe jointly on I_ref and K_{i-1} prevents identity drift—is not quantitatively checked at the keyframe level. The paper states this assumption and shows only qualitative frames in Fig. 3. Because keyframes are generated sequentially, an identity error at K_1 propagates to all later keyframes; the manuscript offers no per-keyframe identity evaluation (e.g., CurricularFace, ArcFace, or DINOv2 scores on K_0,...,K_N) and no comparison with independently generated keyframes. This is load-bearing for the central mechanism and should be measured directly.
  4. [§3.4.2, Eq. (2)] The noise-search reward in Eq. (2) is the cosine similarity between face embeddings of the denoised first frame and I_ref. This is structurally similar to the official identity metrics described in Section 4.1 (CurricularFace and ArcFace for facial subjects). Because the search is performed on the evaluation samples and the reward is a close proxy of the evaluation metric, the identity gain attributed to Noise Searching (0.3853 → 0.4055) may be partly due to reward-metric alignment rather than a general identity-preservation improvement. Please state this overlap explicitly and validate the enhancement with a different identity measure not used during search.
minor comments (5)
  1. [Fig. 2] The figure contains typos: 'promt' should be 'prompt', and 'ID-A ware' appears in the workflow labels. Please correct these.
  2. [Table 2] The official ranking score is reported as 'Score↓' but its definition is never given. Since 'ranked third' is a central claim, the metric used by the leaderboard should be defined or referenced, and its relationship to Eq. (3) clarified.
  3. [Eq. (1)] The spatiotemporal guidance term Δ_stg is defined via 'perturbing selected attention layers,' which is not self-contained. Implementation details give only 'transformer block 29'; please specify which layers are perturbed and how the perturbation is applied.
  4. [§4.1] The text says 'Qwen3.6-27B [28]' but the cited technical report is 'Qwen3'. Please check and correct the model name/version.
  5. [§4.1, Table 1] The identity score aggregates CurricularFace, ArcFace, and DINOv2, but Section 4.2's discussion focuses on face metrics. Clarify how object and animal subjects are scored and whether the aggregation is uniform across categories.

Circularity Check

0 steps flagged

No significant circularity: the central claim is externally grounded in the Track 2 leaderboard; the missing Stage-2 ablation and self-cited noise-search inspiration are methodology concerns, not circular steps.

full rationale

After walking the paper's derivation chain, I find no load-bearing step that reduces a prediction or first-principles result to its own inputs. The headline claim is empirical: 'Our method ranked third on the official Track 2 leaderboard,' which is an external benchmark outcome, not a quantity constructed from the method's own equations. The pipeline components are engineering designs rather than formal derivations, so there is no theorem-to-input equivalence to inspect. The closest candidate is Eq. (2), the noise-search reward R(z)=sim(phi(D(z_T-T')), phi(I_ref)). This is a test-time selection objective, and while it resembles the face-similarity component of the official identity metric, the paper does not claim to predict the identity score from Eq. (2), does not state that phi is the same embedding family as the CurricularFace/ArcFace evaluators, and the component's effect is measured in its own ablation (Table 3). That is a reward-alignment / evaluation-protocol issue, not a construction-level circularity. The citation to ConsistTalk [19] is a self-citation by overlapping authors, but it is used only as an inspiration for noise search and is not load-bearing: the contribution is implemented and measured within this paper. The unablated Stage 2 chained keyframe generation is a missing control for attribution, not a circular argument. No uniqueness-theorem importation, ansatz-smuggling via citation, or renaming of a known result is present. Score 0.

Axiom & Free-Parameter Ledger

6 free parameters · 6 axioms · 0 invented entities

No new physical or architectural entities are introduced; the 'identity anchor' and 'terminal-state prompt' are descriptive concepts within existing models, not new objects with independent falsifiable handles. The load-bearing assumptions are the capability of the off-the-shelf models and the chained keyframe premise, plus hand-set hyperparameters that are not fully specified.

free parameters (6)
  • w_cfg (classifier-free guidance weight) = 5.0
    Manual scale in Eq. 1; no sweep reported; selected on the test set (Sec 4.1).
  • w_stg (spatiotemporal guidance weight) = 1.5
    Manual scale in Eq. 1; perturbs transformer block 29; selected on the test set (Sec 4.1).
  • w_mod (modality guidance weight) = 3.0
    Manual scale in Eq. 1; amplifies the visual boundary-keyframe condition; selected on the test set (Sec 4.1).
  • F_min (base frame budget for shortest segment) = 121
    Hand-set allocation floor in Sec 3.4; other segments scaled proportionally and rounded to 8k+1.
  • K (number of noise-search candidates)
    Sec 3.4.2 says K candidate noise tensors are sampled, but K is never specified; required to reproduce the method.
  • T' (lookahead denoising steps in noise search)
    Sec 3.4.2 says a short lookahead T' << T is used, but the value is never given.
axioms (6)
  • domain assumption The off-the-shelf models (Qwen3.6-27B, Z-Image, LTX-2.3) behave as assumed—e.g., LTX-2.3 keyframe-interpolation mode accepts boundary keyframes, and Z-Image supports joint reference-plus-predecessor conditioning.
    Invoked throughout Sec 3.2–3.4; the paper provides no independent verification of these model capabilities.
  • domain assumption Face-embedding cosine similarity is a valid proxy for identity fidelity in the noise-search reward.
    Eq. 2 uses sim(phi(D(...)), phi(I_ref)); this presumes face embeddings align with perceptual identity for the reward.
  • domain assumption The official IPVG26 metrics (CurricularFace/ArcFace/DINOv2, CLIP, GME, MS, IQ) faithfully measure identity, alignment, and quality.
    Used in Eq. 3 and the leaderboard; not validated inside the paper.
  • ad hoc to paper Chained conditioning on I_ref plus K_{i-1} prevents identity drift across the generated keyframe sequence.
    Central design premise in Sec 3.3; no error-accumulation analysis or per-keyframe identity evaluation is provided.
  • ad hoc to paper LLM terminal-state rewrites provide accurate, unambiguous spatial targets for keyframes.
    Sec 3.2 assumes the rewrite captures body pose, hand position, and gaze at the segment endpoint.
  • standard math Standard diffusion and classifier-free guidance mathematics.
    Eq. 1 uses epsilon_theta predictions and guidance combinations; standard in the cited literature.

reviewed 2026-08-01 · how reviews work

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Cite this review

Pith. "Pith review of Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation." pith.science (2026). https://pith.science/paper/J2XVJSRA

@misc{pith2026260717985,
  author       = {Pith},
  title        = {Pith review of: Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J2XVJSRA}},
  note         = {Machine review of arXiv:2607.17985}
}
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read the original abstract

Identity-preserving text-to-video generation aims to synthesize a video that accurately follows a textual description while maintaining the recognizability of a user-specified subject throughout. The IPVG26 challenge extends this framework from a single holistic prompt to a temporally structured specification. The model additionally receives a sequence of timestamped action captions and must render the subject performing these actions in the specified order. This temporal structure presents a challenge not encountered in previous identity-preserving generation tasks, as the subject must continuously perform a scripted sequence of distinct actions while maintaining a consistent identity. However, end-to-end video generators are prone to appearance drift as motion accumulates and the depicted actions change. We address this challenge with a training-free, three-stage pipeline framework. An action-aware prompt polishment stage first rewrites the inputs into image-generation prompts that specify the terminal state of each action. An identity-preserving generation stage then produces the keyframe sequence by conditioning each frame jointly on the reference identity and its predecessor, thereby decoupling time-invariant appearance from time-varying pose. Finally, an identity-aware inference enhancement stage synthesizes the intermediate segments using multi-reference guidance and identity-driven noise searching, both of which reinforce identity fidelity during sampling. Our method ranked third on the official Track 2 leaderboard, demonstrating competitive performance and strong generality.

Figures

Figures reproduced from arXiv: 2607.17985 by Binyan Chen, Hao Chen, Shangfei Wang, Tong Pan, Zhenjie Liu.

Figure 1
Figure 1. Figure 1: Examples of sequential action identity-preserving text-to-video generation by our framework. The track requires [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed framework consists of three main modules. (a) Action-Aware Prompt Polishment first combines the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative case study. From the reference identity and the global prompt, our pipeline generates the chained [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

38 extracted references · 21 linked inside Pith

  1. [1]

    Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, and Dominik Lorenz

    A. Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, and Dominik Lorenz. 2023. Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.ArXivabs/2311.15127 (2023)

    Pith/arXiv arXiv 2023

  2. [2]

    Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. Arcface: Additive angular margin loss for deep face recognition. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4690–4699

    2019

  3. [3]

    Zhengcong Fei, Debang Li, Di Qiu, Jiahua Wang, Yikun Dou, Rui Wang, Jingtao Xu, Mingyuan Fan, Guibin Chen, Yang Li, et al . 2025. Skyreels-a2: Compose anything in video diffusion transformers.arXiv preprint arXiv:2504.02436(2025)

    Pith/arXiv arXiv 2025

  4. [4]

    Jiayi Gao, Changcheng Hua, Qingchao Chen, Yuxin Peng, and Yang Liu. 2025. Identity-Preserving Text-to-Video Generation via Training-Free Prompt, Image, and Guidance Enhancement.Proceedings of the 33rd ACM International Conference on Multimedia(2025). https://api.semanticscholar.org/CorpusID:281079055

    2025

  5. [5]

    Yoav HaCohen, Benny Brazowski, Nisan Chiprut, Yaki Bitterman, Andrew Kvochko, Avishai Berkowitz, Daniel Shalem, Daphna Lifschitz, Dudu Moshe, Eitan Porat, Eitan Richardson, Guy Shiran, Itay Chachy, Jonathan Chetboun, Michael Finkelson, Michael Kupchick, Nir Zabari, Nitzan Bitton Guetta, N. G. Kotler, Ofir Bibi, Ori Gordon, Poriya Panet, Roi Benita, Shahar...

    Pith/arXiv arXiv 2026

  6. [6]

    Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, and Ofir Bibi

  7. [7]

    Xuanhua He, Quande Liu, Shengju Qian, Xin Wang, Tao Hu, Ke Cao, Keyu Yan, and Jie Zhang. 2024. Id-animator: Zero-shot identity-preserving human video generation.arXiv preprint arXiv:2404.15275(2024)

    Pith/arXiv arXiv 2024

  8. [8]

    Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598(2022)

    Pith/arXiv arXiv 2022

  9. [9]

    Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. 2022. Video diffusion models.Advances in Neu- ral Information Processing Systems35 (2022), 8633–8646

    2022

  10. [10]

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2022. Lora: Low-rank adaptation of large language models.ICLR1, 2 (2022), 3

    2022

  11. [11]

    Teng Hu, Zhentao Yu, Zhengguang Zhou, Sen Liang, Yuan Zhou, Qin Lin, and Qinglin Lu. 2025. Hunyuancustom: A multimodal-driven architecture for cus- tomized video generation.arXiv preprint arXiv:2505.04512(2025)

    Pith/arXiv arXiv 2025

  12. [12]

    Yuge Huang, Yuhan Wang, Ying Tai, Xiaoming Liu, Pengcheng Shen, Shaoxin Li, Jilin Li, and Feiyue Huang. 2020. Curricularface: adaptive curriculum learning loss for deep face recognition. Inproceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5901–5910

    2020

  13. [13]

    Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuan- han Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. 2024. Vbench: Comprehensive benchmark suite for video generative models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 21807– 21818

    2024

  14. [14]

    Junha Hyung, Kinam Kim, Susung Hong, Min-Jung Kim, and Jaegul Choo. 2024. Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling.arXiv preprint arXiv:2411.18664(2024)

    Pith/arXiv arXiv 2024

  15. [15]

    Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu

  16. [16]

    Wei Li, Yiheng Zhang, Fuchen Long, Zhaofan Qiu, Ting Yao, Xiaoyan Sun, and Tao Mei. 2026. ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation. InInternational Conference on Learning Representations

    2026

  17. [17]

    Zhen Li, Mingdeng Cao, Xintao Wang, Zhongang Qi, Ming-Ming Cheng, and Ying Shan. 2024. Photomaker: Customizing realistic human photos via stacked id embedding. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 8640–8650

    2024

  18. [18]

    Lijie Liu, Tianxiang Ma, Bingchuan Li, Zhuowei Chen, Jiawei Liu, Gen Li, Siyu Zhou, Qian He, and Xinglong Wu. 2025. Phantom: Subject-consistent video generation via cross-modal alignment.arXiv preprint arXiv:2502.11079(2025)

    Pith/arXiv arXiv 2025

  19. [19]

    Zhenjie Liu, Jianzhang Lu, Renjie Lu, Cong Liang, and Shangfei Wang. 2025. Con- sistTalk: Intensity Controllable Temporally Consistent Talking Head Generation with Diffusion Noise Search.arXiv preprint arXiv:2511.06833(2025)

    arXiv 2025

  20. [20]

    Ze Ma, Daquan Zhou, Xue-She Wang, Chun-Hsiao Yeh, Xiuyu Li, Huanrui Yang, Zhen Dong, Kurt Keutzer, and Jiashi Feng. 2025. Magic-me: Identity-specific video customized diffusion. InEuropean Conference on Computer Vision. Springer, 19–37

    2025

  21. [21]

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El- Nouby, et al. 2023. DINOv2: Learning Robust Visual Features without Supervision. arXiv preprint arXiv:2304.07193(2023)

    Pith/arXiv arXiv 2023

  22. [22]

    Yingwei Pan, Yiheng Zhang, Zhaofan Qiu, Ting Yao, and Tao Mei. 2026. Identity- Preserving Video Generation Challenge. InProceedings of the 34th ACM Interna- tional Conference on Multimedia

    2026

  23. [23]

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. InInternational conference on machine learning. PmLR, 8748–8763

    2021

  24. [24]

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. 2025. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314(2025)

    Pith/arXiv arXiv 2025

  25. [25]

    Qixun Wang, Xu Bai, Haofan Wang, Zekui Qin, Anthony Chen, Huaxia Li, Xu Tang, and Yao Hu. 2024. Instantid: Zero-shot identity-preserving generation in seconds.arXiv preprint arXiv:2401.07519(2024)

    Pith/arXiv arXiv 2024

  26. [26]

    Yuji Wang, Moran Li, Xiaobin Hu, Ran Yi, Jiangning Zhang, Han Feng, Weijian Cao, Yabiao Wang, Chengjie Wang, and Lizhuang Ma. 2025. Identity-Preserving Text-to-Video Generation Guided by Simple yet Effective Spatial-Temporal De- coupled Representations.Proceedings of the 33rd ACM International Conference on Multimedia(2025). https://api.semanticscholar.or...

    2025

  27. [27]

    Yujie Wei, Shiwei Zhang, Zhiwu Qing, Hangjie Yuan, Zhiheng Liu, Yu Liu, Yingya Zhang, Jingren Zhou, and Hongming Shan. 2024. Dreamvideo: Composing your dream videos with customized subject and motion. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 6537–6549

    2024

  28. [28]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. 2025. Qwen3 technical report.arXiv preprint arXiv:2505.09388(2025)

    Pith/arXiv arXiv 2025

  29. [29]

    Zhuoyi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng, et al . 2024. Cogvideox: Text-to-video diffusion models with an expert transformer.arXiv preprint arXiv:2408.06072(2024)

    Pith/arXiv arXiv 2024

  30. [30]

    Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. 2023. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models.arXiv preprint arXiv:2308.06721(2023)

    Pith/arXiv arXiv 2023

  31. [31]

    Shenghai Yuan, Jinfa Huang, Xianyi He, Yunyang Ge, Yujun Shi, Liuhan Chen, Jiebo Luo, and Li Yuan. 2025. Identity-preserving text-to-video generation by frequency decomposition. InProceedings of the Computer Vision and Pattern Recognition Conference. 12978–12988

    2025

  32. [32]

    Z-Image Team. 2025. Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer.arXiv preprint arXiv:2511.22699(2025)

    Pith/arXiv arXiv 2025

  33. [33]

    Xin Zhang, Yanzhao Zhang, Wen Xie, Mingxin Li, Ziqi Dai, Dingkun Long, Pengjun Xie, Meishan Zhang, Wenjie Li, and Min Zhang. 2024. GME: Improving Universal Multimodal Retrieval by Multimodal LLMs. arXiv:2412.16855 [cs.CV]

    Pith/arXiv arXiv 2024

  34. [34]

    Yunpeng Zhang, Qiang Wang, Fan Jiang, Yaqi Fan, Mu Xu, and Yonggang Qi. 2025. FantasyID: Face Knowledge Enhanced ID-Preserving Video Generation.ArXiv abs/2502.13995 (2025). https://api.semanticscholar.org/CorpusID:276482215

    Pith/arXiv arXiv 2025

  35. [35]

    Zangwei Zheng, Xiangyu Peng, Tianji Yang, Chenhui Shen, Shenggui Li, Hongxin Liu, Yukun Zhou, Tianyi Li, and Yang You. 2024. Open-Sora: Democratizing Efficient Video Production for All.ArXivabs/2412.20404 (2024). https://api. semanticscholar.org/CorpusID:275133398

    Pith/arXiv arXiv 2024

  36. [36]

    Yong Zhong, Zhuoyi Yang, Jiayan Teng, Xiaotao Gu, and Chongxuan Li. 2025. Concat-ID: Towards Universal Identity-Preserving Video Synthesis.arXiv preprint arXiv:2503.14151(2025)

    Pith/arXiv arXiv 2025

  37. [2024]

    LTX-Video: Realtime Video Latent Diffusion.arXiv preprint arXiv:2501.00103 (2024)

    Pith/arXiv arXiv 2024

  38. [2025]

    Vace: All-in-one video creation and editing.arXiv preprint arXiv:2503.07598 (2025)

    Pith/arXiv arXiv 2025

This paper was first reviewed by deepseek-v4-flash on August 1, 2026.