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REVIEW 4 major objections 6 minor 1 cited by

LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper claims that a frozen diffusion transformer video generator can be made to follow the motion of a user-supplied reference video at inference time, with no training, in both text-to-video and image-to-video generation.

desk verdict A plausible training-free DiT motion transfer pipeline whose real-video use case rests on an unvalidated noise-injection shortcut; the merits are real but the evidence needs work. read the letter →

arxiv 2505.14167 v1 pith:TY67CKWD submitted 2025-05-20 cs.CV

Changgu Chen , Xiaoyan Yang , Junwei Shu , Changbo Wang , Yang Li This is my paper
classification cs.CV
keywords diffusiontransformerzero-shotmotiontransfervideogenerationattentioncontroltext-to-videoimage-to-videotraining-freeprior
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that a pre-trained diffusion transformer (DiT) video generator can be controlled at inference time by injecting motion tokens from a reference video, so the generated subject moves like the reference subject while its appearance and background come from the prompt or a start image. This is a zero-shot claim: no weights are updated and no control network is trained. The authors' LMP framework does this for both text-to-video and image-to-video generation, which earlier zero-shot motion-transfer methods built on U-Net attention did not support. If the claim holds, complex motion that is hard to write down in a prompt could instead be specified by example clip, turning the frozen generator into a motion reference tool.

What carries the argument

The load-bearing mechanism is the extended self-attention in each MM-DiT block: the target video's queries attend to both target tokens and reweighted reference foreground keys and values, letting the reference video steer motion without training. Around this sit three modules: Fore-Background Disentangle Module (FBDM), which uses text-video and video-text attention maps to locate the moving subject tokens; Reweighted Motion Transfer Module (RMTM), which scales reference keys by $\lambda=0.98$ and injects them; and Appearance Separation Module (ASM), which optimizes target hidden states to suppress attention to the reference subject prompt when target and reference subjects differ. Real videos are handled by direct noise addition, $z'_t = \lambda_t z'_0 + (1-\lambda_t)\epsilon$, synchronizing the reference denoising with the target.

What would settle it

Take a real reference video, add noise using the paper's Eq. 10, denoise it with the frozen generator, and track the subject; if the reconstructed trajectory differs from the original, the injected tokens do not carry the real motion.

Watch

Extended reading notes

Core claim

Under the DiT architecture, text, image, and video information are all handled as tokens in one self-attention mechanism, so tokens from a reference video can be concatenated into the target video's attention. LMP separates the reference moving subject from its background by reading text-video attention maps; passes only foreground reference keys and values into the target attention, reweighting the reference keys to keep appearance from overshadowing the prompt; and applies a gradient-descent step that lowers attention between the reference subject prompt and target tokens when the two subjects differ. For real reference videos, it skips inversion and instead noisifies the video directly along the same schedule used for generation. The paper reports that on the DAVIS-derived evaluation, LMP outperforms the prior zero-shot motion-transfer method and the base model on motion fidelity, consistency, and prompt alignment.

Load-bearing premise

The fragile step is the assumption that adding noise to a real clip and letting the frozen generator denoise it honestly preserves the clip's motion; if that fails, the extracted tokens do not describe the real movement.

Editorial extensions

If this is right

  • Users could steer generated video motion by example clip, including motions hard to describe in prompts.
  • The same framework works for image-to-video, so a still image of a new subject can be animated with the reference clip's motion.
  • No control-signal preprocessing such as depth, pose, or trajectories is needed, lowering the barrier compared to training-based controllers.
  • Because the base model stays frozen, the method can be applied to newer DiT checkpoints without retraining.
  • Motion transfer stays responsive to prompt edits, so subject and background can be changed independently of the copied motion.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test this, but a reconstruction experiment on real videos would tell whether the direct-noise tokens carry true motion or only low-frequency cues.
  • The same token-injection mechanism could presumably transfer camera motion or object trajectories, not just subject motion, since the foreground-background separation defines what gets injected.
  • If the direct-noise assumption holds, LMP could be combined with other token-level edits to get simultaneous motion and content control in one DiT pipeline.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes LMP, a training-free framework for controlling motion in DiT-based video generation (CogVideoX-5B) by injecting motion tokens extracted from a reference video during inference. Three modules are introduced: a foreground-background disentangle module (FBDM) that uses attention maps to identify foreground tokens, a reweighted motion transfer module (RMTM) that concatenates reference keys/values into the target self-attention with a reweighting factor, and an appearance separation module (ASM) that suppresses reference-subject appearance via gradient descent on hidden states. For real reference videos, the paper proposes directly noising the video with Eq. (10) rather than DDIM inversion. Experiments on a prompt-annotated DAVIS subset compare LMP against CogVideoX and DMT, reporting improved alignment, consistency, PickScore, and a self-defined Motion Fidelity metric, plus qualitative results for text-to-video and image-to-video settings.

Significance. If the central claims hold, LMP would be a useful plug-and-play method for zero-shot motion control on frozen DiT video generators, extending motion transfer to image-to-video without training. The paper explicitly ships a frozen-base-model inference-time algorithm, which is reproducible in principle, and it identifies a real limitation of prior UNet-based motion-transfer methods. However, the significance is tempered by the fact that the quantitative gains over the baseline are small (e.g., Mean Align 25.68 vs 24.84; Mean Pick 20.69 vs 20.46), the comparison set is limited to one prior method (DMT) plus baseline, and the real-video procedure is not validated by a reconstruction experiment. The Motion Fidelity metric is also not fully specified, making the headline motion-control gains difficult to interpret.

major comments (4)
  1. [Sec. 4.5, Eq. (10)] The real-video motion injection procedure is load-bearing and unvalidated. Directly setting z'_t = λ_t·z'_0 + (1-λ_t)·ε and then denoising with the frozen DiT is not an inversion; the paper asserts that this yields a 'controllable' trajectory because at t=0 it recovers z_0, but no experiment shows that the denoised reference video preserves the original subject motion, position, speed, or background-foreground separation. Since the quantitative evaluations on DAVIS and all qualitative real-video examples rely on Eq. (10), the extracted motion tokens may not correspond to the user-provided motion. I request a reconstruction experiment: noise a real video with Eq. (10) at the operating timesteps, denoise with the frozen model, and report how well the denoised output matches the original video's motion (e.g., tracker overlap, trajectory error, or qualitative side-by-side). Without this, the real-video use case and the reported MF gains are not established.
  2. [Sec. 5.1 and Table 1] The quantitative evaluation is underspecified. The paper reports Align, Cons., Pick., and MF but does not state how many DAVIS videos/clips are used, how the test set is split, whether the numbers are averages over multiple seeds, or whether any statistical significance is computed. The Motion Fidelity metric is only described as 'leveraging a pre-trained tracker to monitor subject movement'; the exact formula, normalization, and what constitutes a 'correct' motion match are missing. Given that the headline claim is state-of-the-art motion control, the MF definition and the size/selection of the evaluation set need to be provided, along with error bars or per-video variance. Without this, the small improvements in Table 1 cannot be distinguished from noise.
  3. [Sec. 4.3, 4.4, and 5.1] The core mechanism depends on several hand-set hyperparameters with no sensitivity analysis: the reweighting coefficient λ=0.98 in Eq. (6), the ASM learning rate β=100 in Eq. (9), the stage thresholds T1=40, T2=45, T3=35, and the top-k=1/5 selection in Eq. (8). These values are presented without justification or ablation. Since the method's behavior likely changes sharply with λ and β (e.g., λ too close to 1 may over-inject appearance; β too large may distort the target), a sweep or at least a small ablation table for λ and β is necessary to argue that the reported performance is not a narrow peak. I also note that ASM's gradient descent is an optimization procedure, so the claim in Sec. 4.2 that FBDM incurs 'no additional computational or time overhead' should be qualified; ASM clearly adds per-step gradient computations.
  4. [Sec. 5.3 and Abstract] The claim of 'state-of-the-art performance' is not supported by the comparison set. Table 1 compares only to the baseline CogVideoX and the UNet-based DMT, both of which are not current DiT motion-transfer methods. The related work mentions a DiT-based motion transfer method [25], but it is not compared. To substantiate SOTA status, the paper should compare against at least one additional recent DiT-based or training-free motion-transfer approach, or temper the claim to 'superior to DMT and baseline on our evaluation set.' The current framing overstates what the experiments demonstrate.
minor comments (6)
  1. [General] The manuscript uses an ACM conference template with placeholder text ('Conference acronym XX', 'June 03–05, 2018', 'Received 20 February 2007') and a broken citation for [17]; these formatting issues should be fixed before resubmission.
  2. [Sec. 4.3, Eq. (5)-(6)] The notation is inconsistent: the text introduces \(\hat{K}^v_{ref}\) and \(\hat{V}^v_{ref}\) as 'reference motion tokens,' but Eq. (6) uses the same symbols with a reweighting factor λ applied only to K. It would be clearer to define whether the hats mean the foreground-extracted tokens after FBDM and to state explicitly whether the reweighting applies to the keys only, the values only, or both.
  3. [Algorithm 1] Line 4 calls 'Mapping(z_T, c)' which is not defined in the main text; the mapping from the latent video to the hidden states of the MM-DiT block should be described. Also, the notation REST(h_tar) is used without explanation.
  4. [Sec. 5.1] The sentence 'Following [25, 44], we use the DAVIS dataset' is ambiguous: [25] is a DiT motion transfer paper, but the dataset construction (three parts with modified subject/background prompts) is not described in enough detail to replicate. Please specify how many videos are in each part and what the 'modified subject annotations' look like.
  5. [Sec. 4.2] The FBDM relies on an LLM (qwen2.5-VL-72B-instruct) to generate the reference prompt and on another LLM step to extract subject words. The paper provides no test of the reliability of these LLM-generated prompts; a wrong subject word would break the attention-based foreground extraction. A simple validation (e.g., manually inspecting a sample of extracted subject words) would increase confidence.
  6. [Sec. 5.2, Fig. 5] The image-to-video qualitative examples are limited to two cases. Since image-to-video is a stated novelty, more examples (including failure cases) would strengthen the paper. The current figure does not show whether the reference image's background is preserved or whether the subject's initial pose is respected.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: LMP is an inference-time token-injection method whose design is not derived from its evaluation metrics, and its self-citation is background only.

full rationale

The paper's derivation chain is self-contained: the three modules (FBDM, RMTM, ASM) directly manipulate attention maps and hidden states of a frozen DiT model rather than fitting to the evaluation metrics. The motion-transfer claim is tested against an external baseline (CogVideoX) and a prior method (DMT), with standard CLIP-based alignment and consistency scores plus PickScore; the self-defined Motion Fidelity metric measures the intended behavior but does not appear in the method's construction. The hyperparameters (lambda=0.98, beta=100, T1/T2/T3) are hand-set, which is a robustness concern rather than circularity. The only questionable passage is Section 4.5, Eq. (10), where direct noise addition is asserted to make the denoising trajectory 'controllable'; this is an unvalidated assumption about DDIM-inversion-free conditioning, but it is not circular because it defines the reference-input encoding rather than deriving the output from the metric or from a fitted parameter. The self-citation to Motion-Zero [3] appears only as related-work background and is not load-bearing. Overall, no claimed result reduces by construction to its inputs.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The method adds no new physical entities. Its explanatory burden lies in hand-set hyperparameters and unproved assumptions about DiT attention semantics, LLM annotation quality, and the real-video noise shortcut.

free parameters (5)
  • RMTM reweighting coefficient λ = 0.98
    Hand-set in Section 5.1; controls the balance between target and reference tokens, no sensitivity analysis provided.
  • ASM learning rate β = 100
    Hand-set in Section 5.1; gradient step size for hidden-state suppression, no sensitivity analysis.
  • Denoising stage thresholds T1, T2, T3 = T1=40, T2=45, T3=35
    Hand-set in Section 5.1; schedule for RMTM and ASM usage, and the Algorithm 1 conditions conflict with the text.
  • ASM top-k fraction = 1/5 (k=ceil(n/5))
    Chosen in Section 4.4 to select largest attention values for suppression; no ablation on this fraction.
  • Real-video noise schedule λ_t in Eq. (10) = unspecified
    Section 4.5 defines z'_t = λ_t z'_0 + (1-λ_t) ε but no schedule is given; this is needed to apply the method to real reference videos.
assumptions (4)
  • domain assumption DiT attention maps between prompt tokens and video tokens reliably localize the reference video's moving subject.
    FBDM in Section 4.2 selects foreground tokens by high attention values corresponding to subject words; the paper visualizes this for a dog and a cat but provides no quantitative segmentation accuracy.
  • domain assumption The LLM (qwen2.5-VL-72B-instruct) produces accurate dense prompts and correct subject-word extraction for arbitrary reference videos.
    Section 4.2 and 5.1 rely on the LLM for the reference prompt c' and subject words; failures propagate to FBDM and ASM.
  • ad hoc to paper Adding noise to a real reference video by Eq. (10) and denoising with the frozen DiT yields a trajectory whose tokens represent the original video's motion.
    Section 4.5 asserts this shortcut without a reconstruction experiment; it is not DDIM inversion and is not standard.
  • domain assumption CogVideoX-5B, with keys and values from reference foreground tokens injected, can generate a new subject that follows the reference motion while respecting the text prompt.
    This is the core generative assumption behind RMTM (Section 4.3); the paper's experiments support it qualitatively but not with controlled error analysis.

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Cite this review

Pith. "Pith review of LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer." pith.science (2026). https://pith.science/paper/TY67CKWD

@misc{pith2026250514167,
  author       = {Pith},
  title        = {Pith review of: LMP: Leveraging Motion Prior in Zero-Shot Video Generation with Diffusion Transformer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TY67CKWD}},
  note         = {Machine review of arXiv:2505.14167}
}
read the original abstract

In recent years, large-scale pre-trained diffusion transformer models have made significant progress in video generation. While current DiT models can produce high-definition, high-frame-rate, and highly diverse videos, there is a lack of fine-grained control over the video content. Controlling the motion of subjects in videos using only prompts is challenging, especially when it comes to describing complex movements. Further, existing methods fail to control the motion in image-to-video generation, as the subject in the reference image often differs from the subject in the reference video in terms of initial position, size, and shape. To address this, we propose the Leveraging Motion Prior (LMP) framework for zero-shot video generation. Our framework harnesses the powerful generative capabilities of pre-trained diffusion transformers to enable motion in the generated videos to reference user-provided motion videos in both text-to-video and image-to-video generation. To this end, we first introduce a foreground-background disentangle module to distinguish between moving subjects and backgrounds in the reference video, preventing interference in the target video generation. A reweighted motion transfer module is designed to allow the target video to reference the motion from the reference video. To avoid interference from the subject in the reference video, we propose an appearance separation module to suppress the appearance of the reference subject in the target video. We annotate the DAVIS dataset with detailed prompts for our experiments and design evaluation metrics to validate the effectiveness of our method. Extensive experiments demonstrate that our approach achieves state-of-the-art performance in generation quality, prompt-video consistency, and control capability. Our homepage is available at https://vpx-ecnu.github.io/LMP-Website/

Figures

Figures reproduced from arXiv: 2505.14167 by the authors.

Figure 1
Figure 1. Our LMP framework enables DiT-based video generation models to produce target videos that reference the motion [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The core idea of our FBDM. We achieve disentan [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. The pipeline of our LMP framework in each MM-DiT block. For the first [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The text-to-video results of our LMP framework. The original videos are available in the supplementary material. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: The image-to-video results of our LMP framework. The original videos are available in the supplementary material. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Quality comparison results on different methods in text-to-video setting. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Quality comparison results on different methods in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Quality results of ablation studies. The original [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Attention maps of reference video and target video. [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.