RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers
Authors: Ahmet Berke Gökmen
Yiğit Ekin
Bahri Batuhan Bilecen
Aysegul Dundar
Venue: NeurIPS 2025 poster ()
Keywords: video motion transfer
diffusion transformers
positional embeddings
motion
training-free
video
diffusion
Primary area: deep_learning
TL;DR: We propose RoPECraft, a training-free video motion transfer method for diffusion transformers that operates solely by modifying their rotary positional embeddings (RoPE).

Abstract: We propose RoPECraft, a training-free video motion transfer method for diffusion transformers that operates solely by modifying their rotary positional embeddings (RoPE). We first extract dense optical flow from a reference video, and utilize the resulting motion offsets to warp the complex-exponential tensors of RoPE, effectively encoding motion into the generation process. These embeddings are then further optimized during denoising time steps via trajectory alignment between the predicted and target velocities using a flow-matching objective. To keep the output faithful to the text prompt and prevent duplicate generations, we incorporate a regularization term based on the phase components of the reference video’s Fourier transform, projecting the phase angles onto a smooth manifold to suppress high-frequency artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all recently published methods, both qualitatively and quantitatively.

RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers
"Ahmet Berke Gökmen 1.2
YYgit Ekin'
BBahri Batuhan Bilecen 1,3,4
Aysegul Dundar
Bilkent University INSAIT, Sofia University "St. Kliment Ohridski" 'ETH Zurich
Maa Planck Institute
Reference
RoPECraft
robotic courier zips through maze of an eco-friendly, nutomated warchouse, packages whizzing conveyor belts.
DIM
The silhouctte of ballerina leaps across a sunlit studio, delicate shadow dancing on polished hardwood floors.
A young woman riding a small skatehoard through the misty rainforest.
vintage past snowy station, body steaming frosty
camel walking abandoned, mocalit amphitheater.
vintage biplane loops gracefully shove airfield.
Figure 1: Our method successfully transfers the motion from reference videos.
Abstract
We propose RoPECraft, a training-free video motion transfer method for diffusion transformers that operates solely by modifying their rotary positional embeddings (RoPE). We first extract dense optical flow from reference video, and utilize the resulting motion offsets to warp the complex-exponentaa tensors of RoPE, effectively encoding motion into the generation process. These embeddings are then further optimized during denoising time steps via trajectory alignment between the predicted and target velocities using flow-matching objective. To keep the output faithful to the text prompt and prevent duplicate generations, we incorporate a regularization term based on the phase components of the reference video's Fourier transform, projecting the phase angles onto smooth manifold to suppress high- frequency artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all recently published methods, both qualitatively and guantitatively.
Equal contribution. Correspondence: berke. berke. www.enminsait.aatt ai. Project page
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Diffusion transformers (DiT) have become leading approach for conditional video generation. producing realistic and coherent content across diverse scenarios [6, 20) 50) 21 44). While text conditioning provides a convenient interface, they are often too ambiguous to specify detailed spatio-temporal dynamics such body movement, camera motion, or interactions. As generative quality improves, so does the demand for more precise and controllable motion synthesis.
To address the limitations of text- bast motion control, earlier methods introduced explicit structural cues such as masks, bounding boxes, or depth maps to guide motion g 43 40] These approaches
assume consistent geometry between the reference and generated videos, which often fails under domain shifts 45. More recent work has shifted toward leveraging latent representations within generative models. Some methods extract motion features from internal activations [44] 45 42).
while others modify the latent prior 4 to better align reference and generated motions. prominent example, Go with the Flow (GWTF) 4 uses pretrained optical flow model (37 to generate motion priors that warp the initial noise input while maintaining its Gaussianity. This reportedly stabilizes and speeds up convergence. However, directly warping noise disrupts the intended latent distribution Even with inference- time latent optimization, the method struggles with generalization and domain shifts. As result, GWTF requires costly fine-tuning demanding around 40 GPU days [4]. DiTFlow [31] offers a more efficient alternative by optimizing latents positional embeddings at test time without model retraining. However, it incurs high
of the pre-trained DiT, as seen in Fig. 2
computational costs due to its reliance on a full-size attention-basee feature computation.
We extend DiTFlow's approach by updating only positional embeddings, avoiding latent space devia- tion and content leakage. Our method introduces notion-augmented rotary positional embeddings. warped via optical flow-derived displacements to embed motion cues (Section 4.1). We enhance this with flow-matching-a srreciingggiiedd optimization during early time steps, enabling stable and precise generation (Section 4.2). We further ensure spatiotemporal consistency through Fourier phase regularization (Section 4.3) Unlike GWTF, our method requires no backbone training. drastically cutting computational costs. Compared to DiTFlow, it delivers higher efficiency and better motion quality. addition, to evaluate motion alignment, we propose Fréchet Trajectory Distance (FTD) (Section 5.2
Our method outperforms recent approaches in qualitative and quantitative assessments.
Our contributions are:
An efficient motion transfer, RoPECraft, that leverages motion-augmented rotary positional embeddings in training- frree setting, without requiring any backbone fine-tuning.
A novel use optical flow displacements to warp rotary positional embeddings, encoding spatial motion cues in attention calculations.
A unified optimization strategy combining flow matching velocity prediction and phase constraint regularization to enhance motion accuracy and ensure temporal coherence.
A new evaluation metric, Fréchet Trajectory Distance (FTD), for quantifying motion align- ment between generated and reference videos.
Input
Latent warp
optimization
train DIT
Ours
A motorcycle seen riding track, kicking up smoke it goes.
Figure 2: Latent warping without an expensive fine-tuning of the DIT fails (Column 2), and latent optimization is not adequate to recover the domain shift (Column 3). Our approach keeps the latent space intact, and performs successful motion transfer, all without model re-training (Column 4).

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2 Related Work
Text-to-video models.
Following the successful application of diffusion models image gener- ation [12] 30 (29, (34], efforts have been made to extend this approach to video generation. Initial efforts used U-Net-based architectures for this purpose, utilizing temporal modules [T6] inflated convolutions [ 3
39 to transfer the image prior to the video domain. More recently, DiT 25 50 28 have gained attention due to their superior capabilities in temporal modeling and enhanced quality. Motivated by these, we also adopt DiT backbone.
20
pipelines 21
Motion transfer. The goal of motion transfer is to synthesize videos whose dynamics match reference clip while disentangling motion from appearance. Earlier methods injected explicit structure (masks depth maps) [9] 43 40]. Subsequent work learned dedicated motion embeddings and fed
them to the generator [23
22
Recent approaches exploit the dense motion signals already present in
backbone features 42 15 45
14,, condition trajectories extracted from the reference [48] 46
41) The current state of the art include Go With The Flow [4], which warps the initial noise with reference flow and fine-tunes the DiT this prior, and DiTFlow [II, which derives displacement maps from cross-frame attention and updates either latents or positional embeddings. Building on DiTFlow's insight, dynamically update RoPE guide attention toward reference motion while keeping the backbone frozen. Unlike prior work, we initialize RoPE with our motion-auummentttion algorithm and regularizers, enabling fast and accurate motion transfer. without requiring model masks [14], and high GPU memory during tuning [31).
fine-tuning [4) inversion 442 45
Positional embeddings in vision transformers. Transformers lack inherent order awareness, so Vision Transformers (ViTs) [T] rely on positional embeddings to encode spatial relationships among patches. In early ViT's fixed sinusoidal or learnable absolute embeddings were used [I] g These failed to generalize across varying input resolutions or sequence lengths videos 13. Rotary Position Embedding (RoPE) overcomes these issues by rotating query and key values according to patch positions, thereby capturing relative spatial or temporal relationships (35) Originally successful
in language models 21, RoPE has since been adapted to vision models (27] 17 29 21 44 (3).
Building on these advances, our method updates RoPE embeddings on the fly during generation.
3 Preliminaries
3.1 Flow matching
Flow Matching (FM) is generative modeling approach that learns deterministic, time-dependent velocity field to transform simple base distribution into a complex target distribution (2).. Unlike
diffusion models, which reverse stochastic processes [19], FM minimizes the discrepancy between the model velocity ug (t, z) and a target velocity (z(z) derived from the continuity equation:
LFM E, llo (=)))
(1)
This ensures mass-presesd mussspressriing transport along a predefined probability path pt, enabling more efficient training and sampling than traditional diffusion models.
3.2 Rotary position embeddings (RoPE)
RoPE 35 encodes position by rotating query and key values x in the complex plane, enabling the model to capture relative positional relationships. Given token at position with vector Xmm E gd the vector is split into d/2 pairs. Each pair (xm= (2:-1) xm
(2i), is interpreted as a complex number (e)
Zm xm (2i-1) + xx and RoPE applies the rotation zm Pm... where 4m. eime 25/4 is constructed by Algorithm [] and 0 the base frequency. This operation embeds position into the phase of each frequency component, enabling self-attention to capture relative positions through inner products of queries and keys. Since attention patterns can control motion, we leverage RoPE heavily for our motion transfer task.
3

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Inference
Excoded foumes
Defaut HaPe
Zr
Mt
Optioal Flow Extractor
Waup 4.3
Warped RePE
Phase Constraint
Beference Motin
Video
mtaps
Update according Equation
Optimizaation with flow-matching (Sec
Phase constraint (Sec 4.3)
FFT o
4
Mell
Vg
FFT. Pg claV
d2
min dd d2
Figure 3: Visual description of our proposed pipeline inference and RoPE optimization approach.
Methodology
This section thoroughly explains the proposed components of our motion transfer method. The overall architecture is given in Fig- We augment the RoPE tensors via optical flow maps (Section 4.T). and optimize them during the generation process (Section 4.2) with additional constraints (Section
4.1
Motion-augmented RoPE
Algorithm Default ID RoPE, expanded 3D
1: Input: Base frequency E Rxo
2: Embedding dims D,, Ds, Dw N Sequence lengths Se, Sn. Sw E 4: for each & E (t, h, ) do
p p= 0, 10....... Sk 1jT
D S E R Pu/2
6:
d 1.1..., D2/2 1)
7:
f= = 8 2d/De
b E R 24/2
Alsoritim Alsondinn
Φ edPP
(D
9:
Φ expand +x)
DEC 8EX55
10: end for
11: Φ concat O (.,,,)))
DE X
12: 4 flatten( +)
DE (I
Figure 4: Comparison of the generations of de- fault and motion-augmented RoPE.
The default RoPE algorithm used in video-DiTs is presented in Algorithm where standard ID RoPE independently applied along the temporal (t), height (h), and width (w) dimensions produce the respective components 4.,, 4.. and D u. These are then combined to form the full 3D positional encodings 4, where each dimension
(1, h, w) in 4P& is expanded (repeated) the other two dimensions, (1, h, w \ k. However, as previously discussed, our insight is that this formulation can be altered significantly with motion signals. Specifically, by having unique, motion-augmented ID RoPEs for h and components, allow the attention mechanism during the generation process to better understand which spatial patches should attend to one another.
Our proposed procedure is detailed in Algorithm 2 For each row and column, we use the pro- cessed motion signals hjoww and Wioow to adjust the positional indices p the complex exponential + cxp(ipf"). Unlike Algorithm 1 where 4 is fixed across all rows, and & across all columns, Algorithm Storithm@jntroduuse introduces variation based on the motion signals. This way, we can construct unique embeddings for each spatial row and column for & and 4.... respectively, providing better initial condition for a motion-guided generation. We leave the temporal component 4 unmodified, altering often introduces decoding artifacts without significant benefit.

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Fig. 4 compares the default (Row 3) and modified (Row 4) embeddings visually. It can be observed that Algorithm 2 warps RoPE tensors in the motion direction (Row 1), whose effects are reflected on the generation (Row 2). Fig. 2 demonstrates the effectiveness of our approach across various prompts,
which transfers coarse input motion directly into the generated videos.
Input
Output
Input
Output
Input
Output
Figure 5: Qualitative results of motion aatton- RoPE described in Algorithm 2
However, relying solely on the modification introduced in Algorithm can yield suboptimal results. For example, while the overall motion may be correct, subjects sometimes face the opposite direction (Column fail to accurately follow challanging trajectories (Column 6). To address these limitations, we introduce a brief optimization step over the motion-augmentee RoPE tensors during generation, which will be described in Section 4.2
4.2 Optimization with flow-matching
To refine Algorithm 2 we apply a brief optimiza- tion rotary embeddings during early gener- ation steps. Using Eq. ( we align the gener- ated velocity vg (t zz) with the target velocity Ue ue(x) 0z (xt, v). where is the current latent in time step 6, v is latent reference video, and o is the scheduler sigma.
Algorithm Motion-augmenteed RoPE
1: Input: Base frequency E R2,. Embedding dims D., Dn, Dw E N
3: Sequence lengths S., Sa, Sw E EN
4: Optical flows u, v
D DER D2X
5: 11 doumsampee(u, v)
Iinow Wiloww cumsum v)
Fig. illuutrates the effectiveness of both opti- mization and the motion-augmente RoPE ini- tial condition. In Column 1, the subject moves away from the camera, while in Column 2, the subject moves from left right. The motion- augmented RoPE approach (Columns 3-4) suc- cessfully captures the general movements. How- ever, in the second sample, it incorrectly ren- ders the motorbike facing backward. When opti- mization is performed without a dedicated initial condition (Algorithm D the subject placement improves, but issues arise in motion direction (Column 6), and visual artifacts appear (Column 5, Row 2). In contrast, initializing with Algo- rithm yields the best results across the samples. This approach reduces artifacts, corrects subject orientation and trajectories.
7: haow flatten(haag)
R (8(x8
8:
flatten Witoo))
DE EER (x)
9: G. = 8 g0.. 11000,,...D,/2-111111
DE Pa/2
10: for each in [0, 1,..,.,,,,, S, Sm do
p= [0, 1,...,,., Sn 1]" + haw DER S
12: @/1rr) = pff
D DEC (Dx
13: end for
14: 4 -reorder(#)) D (Da/22)
15: f.. 0 p0. D
16: for each ein 10, 1,... S, x Sn do
17:
p [0, 1, Sw 1 Waow [c]
18:
+ $_[[] iPF
19:
end for
20: 4 - reorder ...)
21: O,.... S 1]
22:
f 0 1//21111/ /
4.3
Phase constraints
23:
4, expand(e/
Flow matching optimization produces strong re- sults, as shown in Fig. 6 but we occasionally observe duplicated subjects when adjusting the orientation, position, or motion of the moving subjects. To address this, we build on insights from prior work 447 and analyze the Fourier transform of our signals. Since linear displacements in the spatial domain cause phase shift in frequency domain,
24: 4 = flatten(concat(@.... (f)
5

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Input
Algorithm -
Algorithm + opt.
Algorithm 2 opt.
Figure 6: Qualitative results on optimization, with identical seeds across different experiments.
a Fourier property closely tied to motion transfer, we add phase constraint to the flow matching objective guide the model toward more accurate and consistent spatiotemporal alignment.
Specifically, we take the Fourier transform of the target velocity u, along spatio-temporal dimension, get F(4)) U, U, exp (j, where JU, {Re( (U.))) the magnitude, is the phase. We perform the same transform to the DiT
and /U, = arctan (Jm(U,) output vo, and add the phase constraint as L1 regularizer the main optimization objective. We represent the phase on the unit circle (exp(j2F())) to make them continuous and differentiable everywhere, since the original mapping LF(-) contains jump discontinuities at -m due to being bounded by (- ni. More clearly, we represent exp(j<F())} cos( (ZF()) + sin( (F))) and perform the phase-consistency loss two parts. The final optimization objective is given in Eq. (2)
min CFM(us, ua) + A|l cos LF((u) - cos <F((v)) + All sin LF(ue) - sin (F(va)ll
(2)
where À is the hyperparameter.
Fig. 7 reveals the effect of phase constraints, fixing duplicate generations and artifacts. Additional
experiments regarding Fourier components are given in Supplementary.
Input
Algorithm 2
opt.
Algorithm
2
opt. w/phase
Figure 7: Qualitative results on phase constraints, with identical seeds across different experiments.
Experimental Results
5.1 Metrics and baselines
We compare method with the recently published motion transfer methods 4 14
31
45 42 For
evaluation, we use videos from the DAVIS dataset (32). We generate 4 diverse prompts per DAVIS
video via ShareGPT4V 7 and LLAMA 3.2 36 which are detailed in the Supplementary.
For the evaluation, we use content-debiased Fréchet Video Distance (CD-FVD) [5] for evaluating fidelity, CLIP similarity [18] for evaluating frame-wisc prompt fidelity using VIT B/32 model [33]. and Motion Fidelity (MF) 45 along with our proposed metric Fréchet Trajectory Distance (FTD) for evaluating the motion alignment between the generated and the ground truth reference motion video. For assessing the motion of the foreground object as well as camera motion, we use FTD by only sampling from the foreground object mask region, and sampling from both the foreground and background mask region. For MF and FTD, the trajectories are obtained using Co- Tracker3 24]
For video synthesis, we use Wan2.1-1.333 338 as the backbone of our method. To obtain a fair
assessment, similar to the approach used DiTFlow (3I), we adapt MOFT (42,,
SMM
45
DitFlow [3 and ConMo [4] to 1. For the methods that require DDIM inversion
42
45 14]

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we perform K-iinjection from reference video latents similar to [3I We evaluate GWTF using their CogVideoX-2B [2] checkpoint. The hyper parameters detailed in Supplementary.
5.2
Fréchet Trajectory Distance
Step 1: Random point sampling
Step 2: Occlusion aware tracking O
Step RMS RD over Trajectories
Trajectoryzake
pozsitdar wide agenire tap
D
Trajectory yena
Greapertall
Figure 8: Fréchet Trajectory Distance (FTD), 1) Sample n foreground (red) and n background (green) seeds on the first frame. 2) Track each seed with occlusion-aware filler: copy the nearest visible neighbor while occluded and discard tracks that never re-appear. 3) Measure the RMS Fréchet distance between generated (fake) and reference (real) tracks.
Discrete Fréchet Distance. Let X E R² be the 2D image coordinate of the Hth point frame t (1 (T), We denote the reference and generated trajectories by T,real= {x ..... x and fikk)) respectively. Then, the discrete Fréchet distance is defined as:
T.fake fake
x
DF (T. ( Taake)
min 0.r:(L,...... ( k=
max
(k) ll
where L is the length of the common re- ce-parameterization, and (a, r) are non-decreasing index maps that allow each curve to pause advance but never step backwards. The inner takes the worst spatial gap along a particular pairing frames, while the outer min selects the pairing that makes this worst gap small as possible. Consequently, Dr is the minimal worst-case deviation between the trajectories after they are aligned time as favorably as the monotone constraint permits.
Fréchet Trajectory Distance (FTD). We utilize Eq. (3) in our proposed FTD metric, along with several tricks to obtain meaningful trajectories T. Fig. 8 explains our procedure thoroughly. From the first frame, uniformly select points inside the binary foreground mask Mo. and n outside to capture both object (red) and background (green) motion. Then, the occlusion-aware tracker [24] generates tracks starting with the initial 2n points. Since some points may go out of bounds or get occluded as the video progresses, we reassign them by copying to their nearest visible neighbor maintain trajectory continuity, and drop a track entirely if the associated point never reappears. Before computing distances, all coordinates are normalized by the frame width W and height H, making the metric resolution-invariant. The procedure yields N valid, temporally coherent pairs (T.real TTlake V 1==1- Utilizing the pairs, we calculate root-mean-square Fréchet distance, FTD = (N-1E, -EN" (T. T ) 0.55 For calculating Dr, we utilize 10
45 computes cosine similarity between frame-to- fo-frame displacements on fixed grid, averaging best matches. However, it ignores path shape, magnitude, and occlusions, and can report high scores even when trajectories diverge. In contrast, our Fréchet Trajectory Distance (FTD) drops unreliable tracks, focuses on relevant regions, and measures curve distance using discrete Fréchet distance, making it more robust to missing data and outliers. As shown in Table [ MF also exhibits much higher standard deviation, highlighting its instability compared to FTD.
Comparison with Motion Fidelity [45]. The Motion Fidelity (MF) metric
To further illustrate the behavioral difference between the two metrics, we present two controlled toy examples in Fig. 9 In Case 1, the generated trajectory follows the same path as the reference but with smaller motion magnitude, while in Case 2, both trajectories share identical motion directions yet are spatially offset. Because the Motion Fidelity (MF) metric normalizes motion vectors to unit length and computes directional cosine similarity over a fixed grid, reports perfect similarity (MF=1.0) in both cases. This normalization causes MF to ignore motion speed, path magnitude, and spatial alignment, effectively making translation-invariant but insensitive to geometric deviation. Moreover, MF can be artificially inflated by nearest-neighbor matches any anywhere in the frame, it measures
7

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average directional alignment between motion vectors rather than actual trajectory correspondence. In contrast, the Fréchet Trajectory Distance (FTD) evaluates the geometric trajectory consistency over time by measuring the curve distance between corresponding paths. FTD therefore penalizes drift, speed changes, and path-shape differences directly. FTD increases proportionally with geometric and temporal discrepancies (FTD=1.4) 1.44 and 1.0 for Cases and 2, respectively). demonstrating its discriminative power and perceptual robustness.
5.3 Qualitative evaluation
Case 1: Same trajectory & Different Magnitude
Case Same trajectory & Different Offset
Fig. T0 provides a visual comparison of the evaluated methods across diverse prompts and motion scenarios. Our approach consistently outperforms others in both trajectory direction and subject orientation. In Pl, MOFT, DitFlow, ConMo, and SMM fail to capture the correct mo- tion direction, although SMM maintains proper subject orientation. In P2, some methods strug- gle with prompt alignment, such as keeping the stationary, and GWTF introduces notice- able artifacts. For more complex motions like P3 and P4, most methods do motion effectively. While GWTF shows motion coherence, it often sacrifices from prompt align- ment. For example, it merges a motorcycle with truck in P3 and does not place the man walking on the wooden dock in P2. A similar issue ap- pears in P6, where GWTF generates distorted motorhome, and only SMM, GWTF, and our method reflect the reference motion correctly. In general, our method accurately captures both motion and subject across all examples. Addi- tional results of our model on challenging videos such as videos with camera motion or multiple subjects can be seen in the Section A.6
the reference
FTD: 1.414 MF: 1.0
FTD: 1.0 MF: 1.0
Generated
Original
Figure 9: Illustration of the behavioral difference between Motion Fidelity (MF) and Fréchet Tra- jectory Distance (FTD) across two controlled toy cases. In both cases, the generated trajectories (red) differ from the original trajectories (blue) either in magnitude (Case 1) or spatial offset (Case 2). Although both pairs exhibit substantial geometric deviation, MF remains artificially high because measures only the directional alignment of motion vectors, discarding scale and positional informa- tion. In contrast, FTD penalizes such discrepancies by accounting for the full geometric path similarity, thereby providing more faithful measure of mo- tion consistency. is important to note that FTD evaluates distance, hence smaller values mean bet- ter motion alignment where MF evaluates direct motion alignment, hence larger values is better.
5.4 Quantitative evaluation
Table compares our method with recent mo- tion transfer baselines across five key metries: Motion Fidelity (MF) [45. content-debiased Fréchet Video Distance (CD-FVD) (5), CLIP similarity [18] and our occlusion-aware FTD on foreground (FG) and all points (FG+BG).
Our method achieves the highest MF score (..5816) and the lowest CD-FVD (1284.58), surpassing a strong baseline, GWTF 4. by +0.0103 (approximately +1.8%) and -200.6 (approximately -13.5%) respectively. It also attains the second- best CLIP similarity (0.2350), and ranks second on both FTD variants, 0.2644 for FG and 0.2584 for FG+BG, while outperforming all remaining competitors. In terms of runtime, our method runs at 109.231 + 3.112 s, comparable to SMM [45 (107.281 + 4.562 s), DitFlow (RoPE) [31] (104.405 + 2.056 s), DitFlow (Latent) [1 (105.126 + 2.739 s), MOFT 42 (119.411 + 3.847 s), and GWTF 4 (101.342 + 3.337 s), while being significantly faster than ConMo 14] (150.666 + 3.317 s).
This demonstrates that our framework achieves strong trade-off between computational efficiency and high-fidelity generation quality.
We also showcase quantitative scores in ablation studies, validating the effectiveness of our design. Specifically, Table justifiss our approach on motion- ROPE, optimization procedure with flow-matching and phase constraints. Table 3 ellaborates on the selection of first / denoising steps in our optimization, and number of optimization steps per We opted for (1, 8) = (10, 5) as we noticed that 10 decreases visual quality significantly.

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PI: A waman rides a bike down wonden dock alongside serene lake al sumrise.
P2: A sailboat anchored tranquil surrounded greenery, the walks along the weathered wonden dock.
P3: The motoreycle drives down dirt mad, and then speeds up as it goes.
large group of fire dancers spinning together circle, with performer leading middle, tropical beach.
P5: A woman wearing black dress walks down worn wooden dock along the edge of misty lake at dusk.
111
silver matorhome pulling campsite nestled among the towering redwoods misty forest.
Figure 10: Qualitative comparison the methods with diverse prompts.

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Table Comparison of motion transfer methods across evaluation metrics. Best and second results are represented with italic and underlined. respectively.
Method
MF f
CD-FVD
CLIP f
FTD (FG) 4
FTD (FG+BG) +
GWTF
4
0.5713-::0.2
1485.23
0.2378:10.04
0.2457=0.14
0.2308:10.10
SMM
45
0.4889:L0.20
1600.33
0.2331
0.2882+0015
0.3176:+0.15
MOFT
42
0.4606-4-0.20
1630.45
0.231140.04
0.281140.16
0.3057+00.1
DitFlow (latents)
31
0,4832:=0.20
1735.49
0.2339=0.04
0.292110.15
0.3135::0.12
DitFlow (RoPE)
31
0.4500=0.18
1852.90
0.2345-0.04
0.2785::0.14
0.3019:1.113
ConMo
0.4627:10.21
1680.78
0.2309=0.04
0.276910.15
0,3040::0.14
Ours 0.581610.19
1284.58
0.235010.00
0.2644_0.14
0.258410.13
Table 2: Ablation on motion-augmented RoPE and phase constraints.
Table 3:
Ablation on hyperparameters.
(f,8)
MF
CD-FVD
CLIP
FTD
Method
MF
CLIP
FTD
5,5
0.5165
1437.99
0,1597
0.2901
Alg
opt. 0.7082 0.1560 0.2174
5,
0.5523
1606.88
0.1663
0.2728
Ale + opt. 0.7092 0.1650 0.2105
10,5
0.5675
1364.25
0.1664
0.2633
Alg + opt. phase 0072 0,1656 0.2060
10, 10
0.6160
1492.86
0.1572
0.2573
5.5
User study
We conducted a user study to evaluate (i) how well our proposed FTD and MF metrics align with human perception, and (ii) overall method quality. We randomly sampled 20 prompt-reference pairs from DAVIS and generated outputs for all competing methods. In the first part of the survey, participants selected the top-3 videos that best matched the reference motion. As shown in Table 4 FTD exhibits a noticeably stronger correlation with human motion judgments than MF. In the second part, ranked their top-3 videos based overall visual preference. The results in Table 5 show that our method is consistently preferred across all evaluated dimensions.
Table 4: Alignment of motion preference.
Table 5: User preference study for visual quality.
FTD (%)
MF (%)
Method RPC GWTF SMM
MOFT
ConMo
DF-L DF-R
choice
25
11
1st 30 %
10%
13%
19%
10%
12%
6%
2nd choice
17
17
2nd 23% %
12%
14%
11%
13%
13%
14%
3rd choice
19
11
3rd 13%
11%
22%
13%
16%
13%
12%
These findings reveal two key outcomes: (1) FTD aligns better with human motion perception than MF, and (2) RoPECraft consistently preferred over all basclines in overall quality.
Conclusion and Discussion
In this paper, we introduce RoPECraft, a training-free motion transfer method that manipulates rotary positional embeddings in diffusion transformers. By combining motion-augmented RoPE tensors, now-matching--aat based optimization, and phase-based regularization, RoPECraft achieves high-quality performance across multiple benchmarks, and produces high-quality motion transfer results.
For the future work, the motion- augmeened RoPE framework can be extended to handle more challenging cases, such as handling motion with extreme occlusion, and better high-frequency details in the generated videos. In addition, the pipeline can be extended controllable video editing.
We discuss limitations and broader impacts in the Supplementary Material.
Acknowledgements. We acknowledge EuroHPC Joint Undertaking for awarding the project ID EHPPCAI--224AA22-03 access Leonardo CINECA, Italy. We also acknowledge Fal.ai for granting GPU access.
10

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For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMss if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [Yes]
Justification: We have utilized LLMs for generating captions of the videos of our dataset and expanding these captions into more diverse prompts. This procedure is explained in detail at Section A.2
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non non-standard components.
Please refer to our LLM policy for what should or should be described.
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A Technical Appendices and Supplementary Material
A.1 Ablation Fourier Features
We ablate the Fourier components for additional constraints on the flow-matching objective on our optimization stage. For the objective min LrM + Le, we ablate two regularizer for magnitude and phase, Eq. (4) and Eq. (5). respectively,
= =1= [F(1))
(4)
All cos LF((u) cos [va)] i|i sin LF(u) sin LF(ve)ll1,
where ue denotes the target velocity, and Vi is the generated velocity output from the transformer
at time step t. As shown in Fig. [T]] the phase-based constraint proves more effective than the magnitude- based one, supporting the discussion the main paper. À is chosen as 1.0 across all experiments, as sweeping À did not result in significant changes.
Reference
w/Magnitude
w/Phase
woman rides bike down wooden dock alongside serene lake sunrise. P2: A silver motorhome pulling up to campsite nestled among the towering redwoods of misty forest.
Figure 11: Qualitative comparison of using magnitude and phase constraints. First column shows the video associated with P1, whereas the second column is with P2.
A.2 Prompts
We extract prompts from DAVIS 32 videos by using ShareGPT4V 77.
For generating diverse prompts, we utilize LLAMA 3.2 36 We construct different system, __e_a_____________ prompts for changing the
object (1), and environment (2). We also provide paraphrased prompt for reconstruction (3). The
system prompts are listed below:
system_prompp_11 Answer with single sentence. You will receive single-sentence or milti-sentence video prompt. Replace its main subject (the actor object performing the action) with new, physically plausible subject while leaving the action, environment, camera movements, and style intact. The new subject must be realistic in the described scenario (e.g., golden retriever border collie; a sports car a vintage motorcycle). Return only the fully rewritten prompt-no explanations, bullet points. Keep the scene information in the prompt the same. Do not just change the gender etc. like man-woman or woman-man. Change the entity class, do not just replace person with person.
Answer with single sentence. You will receive video prompt. Keep the subject(s) and their actions exactly the same, but relocate the scene setting to coherent, vivid new environment. Ensure lighting, weather, and background details match the new setting and remain physically reasonable. Output the updated prompt text and nothing else.
system_prompt
system_prompt.e Answer with a single sentence. not alter the subject, action, scene. Simply rephrase the text it is stylistically
21

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different (synonyms, varied sentence structure) while preserving every factual detail. Return single paraphrased prompt-no commentary, no headings.
For each original prompt from ShareGPT4V 77 corresponding response. The motion prompts utilized in the main paper figures are listed below:
apply these system prompts generate its
A sleek, black helicopter is seen around a bustling beach side promenade, passing by seaside resort building.
A sleek, silver sports car is navigating through the foggy streets of Italian Remaissance-era town perched the edge of rugged cliff overlooking the turquoise Mediterranean Sea.
The video shows a vintage motorcycle driving down track in garage.
woman wearing a beige coat seen browsing bookstore, examining a shelf and then selecting book from the stack.
man is seen sprinting across deserted beach sunset, his feet pounding against the wet sand.
man on a motorcycle is seen riding down a coastal highway with rugged cliffs and rocky outcroppings lining the edge of the ocean, as sunlight catches the spray the waves and casts misty veil over the scene.
backpacker seen walking on a rocky terrain with mountains in the background.
white van is seen driving down street with a building the background.
goose walks on grass and then flies over river.
A.3 Hyperparameters and Computational Requirements
Hyperparameters. For video synthesis, we adopt Wan2.1-1.3B 38) as the backbone our method. Since DiTFlow [3I] was originally proposed Cog VideoX (21), and MOFT [42]. SMM [45], and ConMo [4] were developed on UNet-based architectures, we re-implemented all these baselines using the same Wan2.1-1.33 backbone for fairer comparison. For methods that require DDIM inversion 42 45 14,, we applied key-value (KV) injection into all transformer blocks during the first t denoising steps, following the strategy used in DiTFlow.
conducted extensive experiments to determine optimal hyperparameters for each method Wan2. 1 framework. The hyperparameters are defined as follows: learning rate (l), transformer block index for motion feature extraction (b), number of optimization steps (s), number early denoising steps used for optimization (t, out of 50 total steps), AMF attention temperature for DitFlow (d), and mask fusion weight for ConMo (w). We utilize Adam optimizer across all methods, with their default 3 parameters.
x 10 b= 10, 10, l 5, 2.0 2. MOFT 42 - x 10 -:-0 1=111=001,==00000 10, 10,
1. DiTFlow 31
3. SMM 45
= 1 -0 1
4. ConMo 14
1 = 1 10--006-000000 20, 8 5, = 10, w ==
5. Ours: = 1 10-4 -- 5, 10
For ConMo, we used ground-truth DAVIS masks for the reference videos. We do not extract motion cues from internal layers of the transformer, hence b is not applicable for our case.
Due to limited computational resources, used the original CogVideoX weights provided by the authors for GWTF [4. as training the full 1 pipeline from scratch was infeasible. To align more closely with Wan's 1.3B parameter scale during evaluation, used the 2B checkpoint of GWTE
Computational Requirements. We run all the models on a shared cluster, with compute nodes equipped with 4x NVIDIA A100 64GB.
22

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A.4 Fréchet Trajectory Distance
provide our Fréchet Trajectory Distance pseudocode in Listing
T where the frechetdist
calculated by using [10] and cotracker3 by (24).
Listing Fréchet Trajectory Distance implementation.
import frechetdist
def fill_and droptttttak via):
filled track. clone()
F, filled. shape
5
for in range F):
5
inv_idx ("vis(: tl) non.ero tuple=False) = v-ew(-1)
vis_idr vis(: t] .onzero (as_ (as_tupl==False .wieeeaallee.ee((((_e_. v-ie(-1)
if inv numel () and vis_idx. vis_idx.numel().
numel():
prev_pts = filled (inv_ixx,
curr_pts filled[vis_idx, t]
diddaancemmatrreeeeeeeeeeeeeeeee matrixxpree__pta. curr_pta)
12
filled[inv_idx,
13
else:
14
filled[:, t] filled[:, 1]
dropped ("vis(:, 1:] any nonzero (as_ (as_sttpl==Faaee (iiee==--e))...e(([]]]]
return filled, dropped
def compare_
video1, video2, mask,
n_points=100,
only-Fasee)
20
T, C, H, if use_fg_mask_only
21
queries sample_points. maaiiddemee_aanndollyyymm n_pointa)
else:
queries sample_points, http://ttttrroooaaaaaaaaaaeeeeee randomllaaallllllaaaaaaallll (mask, fg-n.points//2, bg-n.pointa//2)
tracks
drops 0
27
for vid in (video1, video2):
28
pts, vis = cotracker3 queries=queries)
pts, drop fill_anndddee (rop [0)
pts[...,0]] /=
1t /=
tracks. append (pts) drops. apopnd (drop)
34
sq
35
for in range (trecktr [h]_eaa___(1))
if i in drops [0] or i in drops(1]] continue
tracks
tracks [1]
fd frechetdist Q)
sq (qdd
42
return sqrt (mean (sq))
A.5 Limitations and Broader Impacts
Limitations. We present the limitations of method in Fig. 13 These limitations can be mitigated by utilizing a heavier DiT-based video generation model, or a higher-quality motion extractor for motion-augmenttd rotary embedding generation.
limitation of our proposed Fréchet Trajectory Distance is its reliance on CoTracker3 24), which has diffieulty handling zoom in points remain stationary. This limits the accuracy of the extracted trajectories in such scenarios.
out camera motions, especially in cases where the tracking
Broader Impacts. The ability to generate realistic motion in videos can greatly benefit fields such as animation, virtual production, education, and accessibility. However, also introduces risks, particularly in the creation of deepfakes and other forms of synthetic media that may be used
23

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Reference
Output
PI: is seen water skingg the ocean. being pulled boat.
P2: A figure on scooter was observed traversing through the mall before losing balance and subsequently dropping from his vehiele.
Figure 12: Limitations of our method. In the first video, a boat (highlighted with a red rectangle) intermittently appears and disappears, likely due limitations in the backbone network. In the second video, the final frame shows a distorted human figure, caused by the absence of the person in the corresponding frame of the source video. This may highlight limitation of the optical flow extractor used to modify our rotary embeddings, in handling occluded or missing subjects.
to deceive. These concerns highlight the importance responsible use, and supporting research into detection and verification methods to help mitigate potential misuse while enabling positive applications.
A.6 More results on Challenging Videos
To further demonstrate the robustness of our method under challenging conditions, we also evaluate on randomly sampled subsample from RealCam Vid [9]. each containing camera motion and multiple objects. Table [ reveals that our method is also robust on more complex datasets than DAVIS.
Table 6: Comparison of motion transfer methods on RealCam [4
Method
MF
CLIIP
FTD 4
GWTF
0,5796
0.2087
0.2072
ConMo
0,5973
0.2014
0,2800
SMM
0.5717
0.2118
0.2827
DiTFlow (RoPE) 0.5686
0.2118
0.2991
DiTFlow (latent)
0,5693
0.2099
0.2962
MOFT
0.5778
0.2095
0,2873
Ours
0.7019
0.2141
0.1697
24

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Oulpul
Pl: The video shows musicians in studio setting with neutral background
P2: The video shows a pair of mhots on a futuristic spaceship hridge illuminated by neon lights.
depicts a variety of sports luxury cars displayed inside brightly
P4: The video depicts fleet of yachts moored bustling marina under orange evening sky.
PS: The vidco shows two cars, one purple and the other black, displayed on rotating platform inside showroom.
P6: The video shows two yachts, one dark other midnight-blue.
P7: The video shows drome weaving through ncon-lit hoops inside dark warehouse.
P8: The vidco depicts team sled dogs pulling musber across the snow-coverod ground.
video group werne colorful dancing
P10: The video captures young boy bright, sunny day. walking sidewalk with black metal fence, and black leash.
PIL: The video shows two individuals in circular space with wooden structure that has a weoden ceiling and wooden floor.
Figure 13: Our method effectively transfers camera motion and motion from multiple subjects accurately.
25


Reviews:
[{"review_id": "wi4yAmFcqq", "reviewer": "Reviewer_PryD", "summary": "This paper studies the task of performing video-level motion transfer by using priors from video DiT without further test-time training or fine-tuning. It is based on the idea of a prior work called DiTFlow, yet it eliminates the need for optimizing the latent space. The key idea is to infer motion information from estimated optical flows and apply those hints to RoPE positional encodings. A metric called FTD is proposed to evaluate the proposed method.", "questions": "- In almost all comparisons, MOFT / DiTFlow / ConMo have an opposite direction of motion. Is it because of the limitations of the baselines, or are there any issues in re-implementations?\n- Is it possible to provide a theoretical analysis of the proposed methods?\n- A user study might be useful to see both the performance of the proposed pipeline and how the proposed metric aligns with human preferences.\n- How does the proposed method approach videos with camera motion?", "limitations": "This paper relies on a pre-trained optical flow estimator. If the optical flow extracted is not accurate enough, the proposed method will likely fail.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- The studied problem is interesting and important. Achieving motion transfer between different subjects has a lot of applications in various communities.\n\n- The proposed motion transfer method is training-free, which eliminates the need for additional fine-tuning or test-time training.\n- The qualitative results show non-trivial improvements compared to previous methods.\n- The ablation studies are informative to show the effectiveness of different components.\n\n**Weaknesses**\n\n- There is no theoretical guarantee on the correctness of the motion-modulated RoPE algorithm. All results are empirical.\n- The proposed approach only applies to video generative models that use RoPE as their positional encoding in DiT.\n- There is no evidence on how good the proposed FTD metric is. Is it possible to perform a user study to demonstrate how users' preferences align with the proposed FTD metric?", "was_revised": true, "final_justification": "The rebuttal has addressed most of my initial concerns. I will keep a positive score.", "rebuttal": "We thank the reviewer for their positive feedback and for highlighting the significance of our problem setting, the advantages of our training-free approach, the qualitative improvements over prior work, and the value of our ablation studies.\n\n**Regarding the motion directions of other methods:** Our experiments with MOFT, DiTFlow, and ConMo are based directly on the official DiTFlow [CVPR25] codebase. We did not re-implement these methods but used the provided code implementation in Wan 2.1 as-is. We have carefully double-checked the results and can confirm that the observed motion direction differences stem from limitations of the baseline methods, not from any implementation issues.\n\nWhile the motion direction is generally correct across methods, we observe that object orientation is often incorrect in other competing methods. This can be more clearly seen with the background motion, which suggests that motion is being transferred to incorrect subjects. Our flow-matching and phase constraints address these issues, leading to more accurate motion transfer. We will add this discussion to the final manuscript, thank you for pointing it out.\n\n**Regarding theoretical analysis and applicability to RoPE-based models:** We agree that our study is primarily empirical and specifically applies to models using RoPE as their positional encoding within DiT architectures. However, we believe this does not diminish the contribution of our work. RoPE is increasingly adopted in modern generative models, and our experimental insights provide a timely and valuable foundation for future research. There is growing interest in exploring RoPE-based mechanisms in recent literature. For example, recent work has extended its use to camera matrices [1] or there have been recent works that aim to update RoPE for longer video generation [2] or their integration to the video models [3]. Similarly, our approach (modifying RoPE tensors directly with flow values) may inspire further advancements, such as consistent long video generation or video editing.\n\n **Regarding user study:** As suggested, we conducted a user study with 21 participants to evaluate both realism and proposed metrics alignment with human preferences. The user study is conducted as follows: \n\nFor sample selection, we randomly sampled 20 prompts and reference videos from DAVIS and their custom prompts described in the paper, and generated outputs using all compared methods (including ours), and presented users with the resulting videos for each prompt-reference pair. Then, we calculated the FTD and MF score for each generated video-ground truth video pair. For reference, some portion of the data is given below:\n\n| Data | GWTF | RoPECraft | … |  |\n|------|------|-----------|---|--|\n| **Prompt**: A woman wearing a black dress walks down a worn wooden dock along the edge of a misty lake at dusk  **GT video**: lucia.mp4 | FTD and MF scores | FTD and MF scores | FTD and MF scores |  |\n| **Prompt**: A woman rides a bike down a wooden dock alongside a serene lake at sunrise.  **GT video**: bmx-trees.mp4 | FTD and MF scores | FTD and MF scores | MF scores |  |\n\nIn the first part of the survey, users were shown the 7 generated videos per prompt and asked to select the top 3 that best matched the motion in the reference video. Participants were instructed to **focus solely on motion alignment, ignoring visual quality.** This section aimed to compare how well FTD and MF align with human perception of motion. For each prompt and video sample the procedure is as follows:\n\n* Provide all 7 video samples to users, for each question (10 in total). \n* Users pick top-3 samples among 7.\n* For each video, we also get the subset of ground-truth FTD and MF sorted scores.\n* We compare their user and ground-truth match, and assign percentages, to get a sub-table for each user.\n* We average and normalize each sub-table of each user to get the final table.\n\nFor Part 1, on the average of all prompts and all users, the results are as follows:\n|              | Alignment with FTD (%) | Alignment with MF (%) |\n|--------------|------------------------|------------------------|\n| 1st choice   | **25**                 | 11                     |\n| 2nd choice   | **17**                 | 17                     |\n| 3rd choice   | **19**                 | 11                     |\n\nThe table clearly reveals that our proposed FTD metric is more aligned with human perception, further suggesting its success.\n\n**In the second section of the survey,** users were asked to order the best 3 among the outputs based on **visual quality, text prompt alignment, and motion alignment**. This aimed to evaluate overall method quality. For each prompt and video sample the procedure is as follows:\n\n* Provide all 7 video samples to users, for each question (10 in total). \n* Users pick top-3 samples among 7.\n* Average all 3 choices in themselves (each row would sum up to 100%)\n\nFor Part 2, on the average of all prompts and all users, the results are as follows:\n\n|               | **RoPECraft** | **GWTF** | **SMM** | **MOFT** | **ConMo** | **DitFlow (Latents)** | **DitFlow (RoPE)** |\n|---------------|---------------|----------|---------|----------|-----------|------------------------|---------------------|\n| **1st choice** | **30%**       | 10%      | 13%     | 19%      | 10%       | 12%                   | 6%                  |\n| **2nd choice** | **23%**       | 12%      | 14%     | 11%      | 13%       | 13%                   | 14%                 |\n| **3rd choice** | **13%**       | 11%      | 22%     | 13%      | 16%       | 13%                   | 12%                 |\n\n\nThe table shows that our proposed method, RoPECraft, is consistently rated as more visually appealing, accounting for visual quality, text prompt alignment, and motion alignment all at the same time.  RoPECraft was consistently preferred over other methods, indicating that **our approach is better than the current literature in human perception.**\n\nThe results reveal two key insights: (1) FTD correlates better than MF in evaluating motion fidelity, and (2) RoPECraft is consistently rated as more visually appealing while also achieving good motion alignment.\n\nWe would also like to again point out why FTD may be preferred over MF. FTD can be more reliable than MF for evaluating motion alignment because it considers the entire motion trajectory over time, capturing the global structure and temporal consistency of motion. Unlike MF, which focuses on frame-to-frame displacements and overlooks long-term trajectory patterns, FTD evaluates the overall shape of the motion path, making it sensitive to deviations across multiple frames. FTD also effectively handles occlusions by reassigning or discarding invalid trajectories, ensuring that only valid motion paths contribute to the score. In contrast, MF does not account for occlusions or tracking errors, leading to potentially misleading results. Furthermore, while MF normalizes frame-wise displacements and ignores their magnitude, FTD preserves such variations, making it more robust in capturing complex motion dynamics. Overall, we argue that FTD offers a more holistic and accurate reflection of motion similarity, especially in dynamic or occlusion-heavy scenes.\n\n\n**Regarding camera motion:** Our method effectively transfers camera motion. However, due to this year’s new rules, we cannot provide links or a PDF to showcase our results. You will be able to see the additional experiments in the updated Supplementary, in case of acceptance, thank you for your understanding. To provide evidence that our method can handle camera motion and multiple object cases better compared to current baselines, we have randomly sampled 20 videos from RealCamVid [4] dataset ensuring they contain camera motion, multi object and relatively harder samples compared to DAVIS. We have generated 2 prompts per video using the same approach described in the main paper. The computed metrics for these models is as follows:\n\n| Method                  | MF ↑    | CLIP ↑   | FTD ↓ |\n|-------------------------|--------:|---------:|--------------:|\n| ***Ours***                | **0.7019**  | **0.2141**   | **0.1697**        |\n| *GWTF*                | 0.5796  | 0.2087   | 0.2072        |\n| *ConMo*               | 0.5973  | 0.2014   | 0.2800        |\n| *SMM*                 | 0.5717  | 0.2118   | 0.2827        |\n| *DiTFlow (RoPE)*      | 0.5686  | 0.2118   | 0.2991        |\n| *DiTFlow (latent)*    | 0.5693  | 0.2099   | 0.2962        |\n| *MOFT*                | 0.5778  | 0.2095   | 0.2873        |\n\nAs it can be seen, our model surpasses all of its competitors on text and motion alignment in this dataset.\n\n[1] Li et al., Cameras as Relative Positional Encoding, 2025.\n\n[2] RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers, ICML 25\n\n[3] VideoRoPE: What Makes for Good Video Rotary Position Embedding?, ICML 25, Oral\n\n[4] Zheng, Guangcong, et al. \"Realcam-vid: High-resolution video dataset with dynamic scenes and metric-scale camera movements.\" arXiv preprint arXiv:2504.08212 (2025)."}, {"review_id": "2zeQ29n4jL", "reviewer": "Reviewer_Sq4g", "summary": "RoPECraft is a training-free video motion transfer technique for diffusion transformers that works by directly manipulating their rotary positional embeddings (RoPE). It warps RoPE’s complex-exponential tensors using dense optical flow from a reference video to encode motion into generation, then refines these embeddings during denoising via a flow-matching loss that aligns predicted and target velocities. Benchmark evaluations show RoPECraft surpasses recent state-of-the-art methods both qualitatively and quantitatively.", "questions": "N.A.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well written and easy to follow.\n\n2. The idea of modifying rotary positional embeddings (RoPE) for motion transfer is insightful and interesting.\n\nQuestions:\n\n1. All showcased examples and supplementary results focus on single-object motion transfer. Can RoPECraft handle multiple-object motion transfer?\n\n2. During motion transfer, the background often changes significantly, even though the prompt did not include background alteration. Can the method preserve the original background while transferring only the object’s motion?\n\n3. The column indices referenced in Figure 2’s caption appear misleading—please correct them in the final version.", "was_revised": true, "final_justification": "The authors have addressed my questions and concerns, I will keep my positive score.", "rebuttal": "We thank the reviewer for their positive feedback and for recognizing our work as insightful, interesting, and well presented.\n\n**Regarding multi-object cases:** RoPECraft is capable of handling multi-object motion transfer. However, due to this year’s new rules, we cannot provide links or a PDF to showcase our results. You will be able to see the additional experiments in the updated Supplementary, in case of acceptance, thank you for your understanding. To provide evidence that our method can handle camera motion and multiple object cases better compared to current baselines, we have randomly sampled 20 videos from RealCamVid [1] dataset ensuring they contain camera motion, multi object and relatively harder samples compared to DAVIS. We have generated 2 prompts per video using the same approach described in the main paper. The computed metrics for these models is as follows:\n\n| Method                  | MF ↑    | CLIP ↑   | FTD ↓ |\n|-------------------------|--------:|---------:|--------------:|\n| ***Ours***                | **0.7019**  | **0.2141**   | **0.1697**        |\n| *GWTF*                | 0.5796  | 0.2087   | 0.2072        |\n| *ConMo*               | 0.5973  | 0.2014   | 0.2800        |\n| *SMM*                 | 0.5717  | 0.2118   | 0.2827        |\n| *DiTFlow (RoPE)*      | 0.5686  | 0.2118   | 0.2991        |\n| *DiTFlow (latent)*    | 0.5693  | 0.2099   | 0.2962        |\n| *MOFT*                | 0.5778  | 0.2095   | 0.2873        |\n\nAs it can be seen, our model surpasses all of its competitors on text and motion alignment in this dataset.\n\n**Regarding preserving background:** The goal of motion transfer is to transfer the motion from a reference video to a newly generated one, guided by a text prompt. While the prompt may not always specify the background, DiT models (owing to their attention mechanisms and training data) naturally generate coherent and realistic backgrounds. \n\nThat said, by applying flow-matching optimization for longer and using background masks, it is possible to preserve the original background while transferring only the motion. In fact, we verified that it works with our parameters in Supplementary A.3; but with the mask and by changing s=30 and t=20 for this sub-task. We will include the additional results to the Supplementary, thank you for suggesting this.\n\n**Regarding typos:** In Fig. 2, the caption for Column 4 should read Column 5. We will go through the manuscript and fix the errors, thank you.\n\n[1] Zheng, Guangcong, et al. \"Realcam-vid: High-resolution video dataset with dynamic scenes and metric-scale camera movements.\" arXiv preprint arXiv:2504.08212 (2025)."}, {"review_id": "casJ5dkgKg", "reviewer": "Reviewer_9b3n", "summary": "The authors propose a training-free motion transfer methods. The core idea of the method lies in leveraging optical flow warp RoPE embeddings for the target video. To improve results, the authors further fine-tune the embeddings at test time using a flow matching objective regularized with a phase regularization term. A new evaluation metric is proposed and a good amount of qualitative results are provided that show good output quality. Quantitative evaluation places the method at the level of the current state of the art.", "questions": "- The method seems to improve performance qualitatively over the current state of the art. This is not captured by the contributed metric. It is thus unclear where the proposed method stands with respect to the state of the art, and whether the proposed metric is a good metric. This prevents the reader both from understanding quality of the proposed method and whether FTD is an informative metric, making the significance of the work unclear. I think both these points could be addressed with user studies showing that: 1. Overall the proposed method is preferred to the current state of the art despite having poorer motion alignment quality as shown by FTD, 2. FTD correlates to motion alignment quality better than MF. If these two points can be shown I would reconsider my score.", "limitations": "- Limitations are adequately discussed", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "QUALITY\n- The paper shows a good qualitative evaluation section as part of the supplementary which shows good method performance with respect to the current state of the art\n- Authors propose as a contribution Frechet Trajectory Distance (FTD) as a more reliable of motion alignment than MF. In Tab. 1, however, GWTF outperforms the proposed method in this very metric. This raises questions on whether either the proposed metric contribution is not representative of motion quality or whether the proposed method does not beat the baseline in this regard. Looking at the qualitative results, it seems the proposed method produces more realistic results, while GWTF more strictly adheres to the conditioning motion. I'd like the authors to clarify this point\n- Given the uncertainty arisen by automated quantitative metrics, a user study would have enhanced thoroughness of the evaluation section, validating both the effectiveness of the proposed method and metric contributions\n\n\nCLARITY\n- The paper is well written and easy to follow. Algorithms are presented to clarify the implementation of the main method components. The supplementary material describes training hyper parameters and the proposed FTD metric implementation in detail.\n\n\nSIGNIFICANCE\n- Motion transfer is a an established research direction and the proposed method seemingly improves over the current state of the art, at least qualitatively. However, the incongruent performance of the proposed method with respect to the contributed FTD metric, compounded by the absence of a user study evaluating metric and method effectiveness, make the effectiveness of the method and proposed metric unclear to the reader.\n\nORIGINALITY\n- The idea of transfering motion by altering RoPE embeddings is interesting and the authors explore it deeply, showing the necessity of embedding fine-tuning and phase regularization. This contribution is original to the best of my knowledge", "was_revised": true, "final_justification": "The rebuttal clarified my doubts regarding the proposed metrics and performance of the method with respect to baselines. The inclusion of the user studies for both evaluation metrics and proposed method quality strengthen the evaluation and demonstrate the significance of the work. I thus raise my score to recommend acceptance.", "rebuttal": "We thank the reviewer for their feedback, finding our work easy to follow, original, and interesting.\n\n**Regarding FTD:** We agree with the observation that RoPECraft produces more realistic results, whereas GWTF adheres more strictly to the conditioning motion. This likely explains why GWTF slightly outperforms RoPECraft on the FTD metric, as noted by the Reviewer. Since FTD is specifically designed to evaluate motion alignment rather than visual realism, the difference in scores reflects this trade-off.  However, our results also show that strict adherence to the conditioning motion does not always lead to better outcomes.  GWTF has higher adherence to the reference motion, we observe that this is not an advantage for GWTF, leading to distortions and conflicts with the text prompt. For example, when generating a flock of animals from a single-object reference video, GWTF often overfits to the single instance, producing unrealistic and distorted outputs (see “CogVideoX results” on the website, in the supplementary ZIP).\n\nWe believe that both motion adherence (e.g., FTD) and realism/naturalness (e.g., user study and CD-FVD scores) are essential for a holistic evaluation of motion transfer methods. This is a very important point, and we will include this discussion in the final manuscript, thank you for the suggestion.\n\n**Regarding user study:** As suggested, we conducted a user study with 21 participants to evaluate both realism and proposed metrics alignment with human preferences. The user study is conducted as follows: \n\nFor sample selection, we randomly sampled 20 prompts and reference videos from DAVIS and their custom prompts described in the paper, and generated outputs using all compared methods (including ours), and presented users with the resulting videos for each prompt-reference pair. Then, we calculated the FTD and MF score for each generated video-ground truth video pair. For reference, some portion of the data is given below:\n\n| Data | GWTF | RoPECraft | … |  |\n|------|------|-----------|---|--|\n| **Prompt**: A woman wearing a black dress walks down a worn wooden dock along the edge of a misty lake at dusk  **GT video**: lucia.mp4 | FTD and MF scores | FTD and MF scores | FTD and MF scores |  |\n| **Prompt**: A woman rides a bike down a wooden dock alongside a serene lake at sunrise.  **GT video**: bmx-trees.mp4 | FTD and MF scores | FTD and MF scores | MF scores |  |\n\nIn the first part of the survey, users were shown the 7 generated videos per prompt and asked to select the top 3 that best matched the motion in the reference video. Participants were instructed to **focus solely on motion alignment, ignoring visual quality.** This section aimed to compare how well FTD and MF align with human perception of motion. For each prompt and video sample the procedure is as follows:\n\n* Provide all 7 video samples to users, for each question (10 in total). \n* Users pick top-3 samples among 7.\n* For each video, we also get the subset of ground-truth FTD and MF sorted scores.\n* We compare their user and ground-truth match, and assign percentages, to get a sub-table for each user.\n* We average and normalize each sub-table of each user to get the final table.\n\nFor Part 1, on the average of all prompts and all users, the results are as follows:\n|              | Alignment with FTD (%) | Alignment with MF (%) |\n|--------------|------------------------|------------------------|\n| 1st choice   | **25**                 | 11                     |\n| 2nd choice   | **17**                 | 17                     |\n| 3rd choice   | **19**                 | 11                     |\n\nThe table clearly reveals that our proposed FTD metric is more aligned with human perception, further suggesting its success.\n\n**In the second section of the survey,** users were asked to order the best 3 among the outputs based on visual quality, text prompt alignment, and motion alignment. This aimed to evaluate overall method quality. For each prompt and video sample the procedure is as follows:\n\n* Provide all 7 video samples to users, for each question (10 in total). \n* Users pick top-3 samples among 7.\n* Average all 3 choices in themselves (each row would sum up to 100%)\n\nFor Part 2, on the average of all prompts and all users, the results are as follows:\n\n|               | **RoPECraft** | **GWTF** | **SMM** | **MOFT** | **ConMo** | **DitFlow (Latents)** | **DitFlow (RoPE)** |\n|---------------|---------------|----------|---------|----------|-----------|------------------------|---------------------|\n| **1st choice** | **30%**       | 10%      | 13%     | 19%      | 10%       | 12%                   | 6%                  |\n| **2nd choice** | **23%**       | 12%      | 14%     | 11%      | 13%       | 13%                   | 14%                 |\n| **3rd choice** | **13%**       | 11%      | 22%     | 13%      | 16%       | 13%                   | 12%                 |\n\n\nThe table shows that our proposed method, RoPECraft, is consistently rated as more visually appealing, accounting for visual quality, text prompt alignment, and motion alignment all at the same time.  RoPECraft was consistently preferred over other methods, indicating that **our approach is better than the current literature in human perception.**\n\nThe results reveal two key insights: (1) FTD correlates better than MF in evaluating motion fidelity, and (2) RoPECraft is consistently rated as more visually appealing while also achieving good motion alignment.\n\nWe would also like to again point out why FTD may be preferred over MF. FTD can be more reliable than MF for evaluating motion alignment because it considers the entire motion trajectory over time, capturing the global structure and temporal consistency of motion. Unlike MF, which focuses on frame-to-frame displacements and overlooks long-term trajectory patterns, FTD evaluates the overall shape of the motion path, making it sensitive to deviations across multiple frames. FTD also effectively handles occlusions by reassigning or discarding invalid trajectories, ensuring that only valid motion paths contribute to the score. In contrast, MF does not account for occlusions or tracking errors, leading to potentially misleading results. Furthermore, while MF normalizes frame-wise displacements and ignores their magnitude, FTD preserves such variations, making it more robust in capturing complex motion dynamics. Overall, we argue that FTD offers a more holistic and accurate reflection of motion similarity, especially in dynamic or occlusion-heavy scenes."}, {"review_id": "HyChJvsOQN", "reviewer": "Reviewer_d2Er", "summary": "The authors propose a novel training-free motion transfer method. Specifically, they extract optical flows from the source video and apply a warping operation to the RoPE embedding to incorporate motion into the generation process. The warped embeddings are then optimized during the denoising steps. Additionally, they introduce a new regularization term based on the phase angle of the source video’s Fourier transform to suppress visual artifacts. Experimental results are conducted on standard benchmarks to demonstrate the effectiveness of their approach.", "questions": "Please refer to the weaknesses for my questions on this paper.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- Novelty. The authors propose a novel motion transfer method that leverages optical flow to warp rotary positional embeddings.\n- Competitive results. The proposed method achieves competitive performance in both qualitative and quantitative evaluations.\n- Clarity. The paper is clearly written and well-organized.\n\n**Weaknesses**\n\n- The paper does not include runtime comparisons between the proposed method and existing approaches. Including this information would be valuable for assessing the method’s practicality in real-world applications.\n- It would be beneficial to present qualitative results of the method on more challenging scenarios, such as the breakdancing sequences in the DAVIS dataset. Analyzing performance on these difficult cases would provide deeper insights into the task’s complexity and the robustness of the proposed approach.", "was_revised": true, "final_justification": "The proposed method is novel and has achieved competitive results. The authors also formulate the paper with clarity. In the rebuttal, the author has addressed my concerns regarding the runtime and hard case results. Therefore, I would like to keep my score as borderline accept.", "rebuttal": "We thank the reviewer for their feedback, finding our work novel and clear.\n\n**Regarding runtime comparisons:** \nOver a total of 360 individual runs each taken place on WAN 2.1 based our implementation with floating point 16 precision. Each run has been executed in a single H100 gpu. the configuration of each run is as follows:\n* number of optimization steps = 5 \n* height of video = 480\n* width of video = 832 \n* number of frames = 49\n* number of inference steps = 50\n\nAccording to this configuration, you can find the runtime information in the table below:\n\n|                   |        Conmo        |                 SMM | DitFlow (Rope)      | DitFlow (Latent)    | Moft                | GWTF | Ours |\n| :---------------- | :-----------------: | ------------------: | ------------------- | ------------------- | ------------------- | ---- |  ---- |\n| Runtime (seconds) | 150.666 ± 3.317 | 107.281 ± 4.562 | 104.405 ± 2.056 | 105.126 ± 2.739 | 119.411 ± 3.847 | 101.342 ± 3.337 | 109.231 ± 3.112 |\n\nAs shown in the table, all methods except ConMo have comparable runtimes, with our method running at 109.2 ± 3.1 seconds. While not the fastest, it remains within the typical range observed across recent approaches, balancing efficiency with improved realism. We appreciate the reviewer’s suggestion regarding runtime analysis. We will add the comparison table and a brief discussion in the revised manuscript.\n\n**Regarding more challenging datasets:** Our method is also successful in more challenging scenarios beyond the DAVIS dataset, such as RealCamVid [1]. However, due to this year’s new rules, we cannot provide links or a PDF to showcase our results. You will be able to see the additional experiments in the updated Supplementary, in case of acceptance, thank you for your understanding. To provide evidence that our method can handle camera motion and multiple object cases better compared to current baselines, we have randomly sampled 20 videos from RealCamVid [1] dataset ensuring they contain camera motion, multi object and relatively harder samples compared to DAVIS. We have generated 2 prompts per video using the same approach described in the main paper. The computed metrics for these models is as follows:\n\n| Method                  | MF ↑    | CLIP ↑   | FTD ↓ |\n|-------------------------|--------:|---------:|--------------:|\n| *Ours*                | 0.7019  | 0.2141   | 0.1697        |\n| *GWTF*                | 0.5796  | 0.2087   | 0.2072        |\n| *ConMo*               | 0.5973  | 0.2014   | 0.2800        |\n| *SMM*                 | 0.5717  | 0.2118   | 0.2827        |\n| *DiTFlow (RoPE)*      | 0.5686  | 0.2118   | 0.2991        |\n| *DiTFlow (latent)*    | 0.5693  | 0.2099   | 0.2962        |\n| *MOFT*                | 0.5778  | 0.2095   | 0.2873        |\n\nAs it can be seen, our model surpasses all of its competitors on text and motion alignment in this dataset.\n\nIn addition, regarding the **breakdance example in the DAVIS dataset**, our method performs significantly better than the others. We appreciate the reviewer’s suggestion and will add harder examples in the revised manuscript.\n\n[1] Zheng, Guangcong, et al. \"Realcam-vid: High-resolution video dataset with dynamic scenes and metric-scale camera movements.\" arXiv preprint arXiv:2504.08212 (2025)."}]

Author rebuttal:

Meta-review: This paper was reviewed by four experts in the field. Most concerns are solved in the rebuttal and discussions. The paper finally received borderline positive reviews with 4 Borderline Accepts. 

This paper proposes a training-free video motion transfer technique by directly manipulating their RoPE.
It first warps RoPE’s complex-exponential tensors using optical flow from the input video to provide motion guidance.
It then optimizes the embeddings at test time via a flow-matching loss that aligns predicted and target input video's velocitie, and a phase
constraint regularization to ensure temporal coherence. A metric called FTD is proposed to evaluate the proposed method.
Experimental results show that the proposed method outperforms other baselines. 

**strengths**
- the task has a lot of applications in various communities
- a training-free method, the idea of warp  rotary positional embeddings for motion transfer is simple yet effective
- non-trivial improvements compared to previous methods
- well written and easy to follow

**weaknesses**
- there is no evidence on how the proposed metric FTD aligns with the human preference, whether FTD is an informative metric
- insufficient experimental validation: missing performance on camera motion transfer, missing results on multiple-object motion transfer, missing runtime comparisons


According to the reviews, the reviewers acknowledge the paper's good results and novel idea. The missing experimental validations are provided in the rebuttal. The AC appreciates the very comprehensive user study to validate the effectiveness of FTD and the superiority of the proposd method over other baselines. Following the rebuttal, reviewers have provided relatively positive ratings for the paper. In the revision, the authors are suggested to incorporate the reviewers' feedback, to include the required experimental results.
Decision: Accept (poster)