Title: RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers
Venue: NeurIPS 2025
Accepted: yes
Keywords: video motion transfer

diffusion transformers

positional embeddings

motion

training-free

video

diffusion
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

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 highfrequency artifacts. Experiments on benchmarks reveal that RoPECraft outperforms all recently published methods, both qualitatively and quantitatively.

1 Introduction

Diffusion transformers (DiT) have become a leading approach for conditional video generation, producing realistic and coherent content across diverse scenarios [6, 16, 20, 50, 21, 38, 44]. While text conditioning provides a convenient interface, they are often too ambiguous to specify detailed spatio-temporal dynamics such as 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-based motion control, earlier methods introduced explicit structural cues such as masks, bounding boxes, or depth maps to guide motion [9, 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 [14, 45, 42], while others modify the latent prior [4] to better align reference and generated motions. A prominent example, Go with the Flow (GWTF) [4], uses a 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 of the pre-trained DiT, as seen in Fig. 2. Even with inference-time latent optimization, the method struggles with generalization and domain shifts. As a result, GWTF requires costly fine-tuning, demanding around 40 GPU days [4]. DiTFlow [31] offers a more efficient alternative by optimizing latents or positional embeddings at test time without model retraining. However, it incurs high computational costs due to its reliance on a full-size attention-based feature computation.

We extend DiTFlow's approach by updating only positional embeddings, avoiding latent space deviation and content leakage. Our method introduces motion-augmented rotary positional embeddings, warped via optical flow-derived displacements to embed motion cues (Section 4.1). We enhance this with flow-matching-guided optimization during early time steps, enabling stable and precise generation (Section 4.2). We further ensure spatiotemporal consistency through a 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. In 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 a training-free setting, without requiring any backbone fine-tuning.

A novel use of 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 alignment between generated and reference videos.

Input

Latent warp

+ optimization

+ train DiT

Ours

2 Related Work

Text-to-video models. Following the successful application of diffusion models in image generation [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 [16] or inflated convolutions [1, 3, 20, 39] to transfer the image prior to the video domain. More recently, DiT pipelines [21, 44, 38, 25, 50, 28] have gained attention due to their superior capabilities in temporal modeling and enhanced quality. Motivated by these, we also adopt a DiT backbone.

Motion transfer. The goal of motion transfer is to synthesize videos whose dynamics match a reference clip while disentangling motion from appearance. Earlier methods injected explicit structure (masks or 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], or condition on 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 on this prior, and DiTFlow [31], which derives displacement maps from cross-frame attention and updates either latents or positional embeddings. Building on DiTFlow's insight, we dynamically update RoPE to guide attention toward reference motion while keeping the backbone frozen. Unlike prior work, we initialize RoPE with our motion-augmentation algorithm and regularizers, enabling fast and accurate motion transfer, without requiring model fine-tuning [4], inversion [42, 45, 14], masks [14], and high GPU memory during tuning [31].

Positional embeddings in vision transformers. Transformers lack inherent order awareness, so Vision Transformers (ViTs) [11] rely on positional embeddings to encode spatial relationships among patches. In early ViT's fixed sinusoidal or learnable absolute embeddings were used [11, 8]. These failed to generalize across varying input resolutions or sequence lengths in videos [8, 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 [35, 2], RoPE has since been adapted to vision models [27, 17, 29, 21, 44, 38]. Building on these advances, our method updates RoPE embeddings on the fly during generation.

3 Preliminaries

3.1 Flow matching

Flow Matching (FM) is a generative modeling approach that learns a deterministic, time-dependent velocity field to transform a simple base distribution into a complex target distribution [26]. Unlike diffusion models, which reverse stochastic processes [19], FM minimizes the discrepancy between the model velocity v θ ( t, x ) and a target velocity u t ( x ) derived from the continuity equation:

This ensures mass-preserving transport along a predefined probability path p t , 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 a token at position m with vector x m ∈ R d , the vector is split into d/ 2 pairs. Each pair ( x (2 i -1) m , x (2 i ) m ) is interpreted as a complex number z ( i ) m = x (2 i -1) m + j x (2 i ) m , and RoPE applies the rotation z ( i ) m · Φ m,i , where Φ m,i = e jmθ -2 i/d is constructed by Algorithm 1, and θ is 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.

Reference Motion

Video

Optical

Flow

Extractor

Optical flow

maps

Warp

Warped

RoPE

E

DiT

Default RoPE

𝑍𝑇

𝑣𝜃

𝑢𝑡

Optimization with flow-matching (Sec 4.2)

Phase constraint (Sec 4.3)

𝐿𝐹𝑀 =

𝑣𝜃 - 𝑢𝑡

2

𝑣𝜃

𝑢𝑡

-

Inference

𝑢𝑡

𝑣𝜃

𝐿𝐹𝑀

+

𝑃ℎ𝑎𝑠𝑒 𝐶𝑜𝑛𝑠𝑡𝑟𝑎𝑖𝑛𝑡

𝑈𝑝𝑑𝑎𝑡𝑒 𝑎𝑐𝑐𝑜𝑟𝑑𝑖𝑛𝑔 𝑡𝑜 𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2

∠ 𝑢𝑡

FFT

∠ 𝑣𝜃

FFT

𝑒 j∠ 𝑢𝑡

𝑒 j∠ 𝑉 𝜃

𝑑1

𝑑2

min𝜆 𝑑1 + 𝜆 𝑑2

Encoded

frames

xN

茶

4 Methodology

This section thoroughly explains the proposed components of our motion transfer method. The overall architecture is given in Fig. 3. We augment the RoPE tensors via optical flow maps (Section 4.1), and optimize them during the generation process (Section 4.2) with additional constraints (Section 4.3).

4.1 Motion-augmented RoPE

Algorithm 1 Default 1D RoPE, expanded to 3D

1: Input: Base frequency θ ∈ R > 0

2: Embedding dims D t , D h , D w ∈ N

3: Sequence lengths S t , S h , S w ∈ N

4:

for

each

k

∈ {

t, h, w

}

do

5:

p

= [0

,

1

, . . . , S

k

-

1]

T

▷

∈

R

S

k

6:

d

= [0

,

1

, . . . , D

k

/

2

-

1]

T

▷

∈

R

D

k

/

2

7:

f

=

θ

-

2

d

/D

k

▷

∈

R

D

k

/

2

8:

Φ

k

=

e

j

pf

T

▷

∈

C

S

k

×

(

D

k

/

2)

9:

Φ

k

=

expand

(

Φ

k

)

▷

∈

C

S

t

×

S

h

×

S

w

×

(

D

k

/

2)

10:

end for

11:

Φ

=

concat

(

Φ

t,h,w

)

▷

∈

C

S

t

×

S

h

×

S

w

×

(

D/

2)

12:

Φ

=

flatten

(

Φ

)

▷

∈

C

1

×

1

×

(

S

t

S

h

S

w

)

×

(

D/

2)

Reference

Generated

Algorithm 1

Algorithm 2

The default RoPE algorithm used in video-DiTs is presented in Algorithm 1, where standard 1D RoPE is independently applied along the temporal ( t ), height ( h ), and width ( w ) dimensions to produce the respective components Φ t , Φ h , and Φ w . These are then combined to form the full 3D positional encodings Φ , where each dimension k ∈ { t, h, w } in Φ k is expanded (repeated) in the other two dimensions, { t, 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 1D RoPEs for h and w components, we 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 processed motion signals h flow and w flow to adjust the positional indices p in the complex exponential Φ = exp( j pf T ) . Unlike Algorithm 1, where Φ h is fixed across all rows, and Φ w across all columns, Algorithm 2 introduces variation based on the motion signals. This way, we can construct unique embeddings for each spatial row and column for Φ h and Φ w , respectively, providing a better initial condition for a motion-guided generation. We leave the temporal component Φ t unmodified, as altering it often introduces decoding artifacts without significant benefit.

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. 5 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

However, relying solely on the modification introduced in Algorithm 2 can yield suboptimal results. For example, while the overall motion may be correct, subjects sometimes face the opposite direction (Column 4) or fail to accurately follow challanging trajectories (Column 6). To address these limitations, we introduce a brief optimization step over the motion-augmented RoPE tensors during generation, which will be described in Section 4.2.

4.2 Optimization with flow-matching

Algorithm 2 Motion-augmented RoPE

1:

Input:

Base frequency

θ

∈

R

>

0

,

2:

Embedding dims

D

t

, D

h

, D

w

∈

N

3:

Sequence lengths

S

t

, S

h

, S

w

∈

N

4:

Optical flows

u

,

v

▷

∈

R

2

×

S

t

×

H

×

W

5:

u

,

v

=

downsample

(

u

,

v

)

▷

∈

R

2

×

S

t

×

S

h

×

S

w

6:

h

flow

,

w

flow

=

cumsum

(

u

,

v

)

7:

h

flow

=

flatten

(

h

flow

)

▷

∈

R

(

S

t

×

S

w

)

×

S

h

8:

w

flow

=

flatten

(

w

flow

)

▷

∈

R

(

S

t

×

S

h

)

×

S

w

9:

f

h

=

θ

-

2[0

,

1

,...,D

h

/

2

-

1]

T

/D

h

▷

∈

R

D

h

/

2

10:

for

each

r

in

[0

,

1

, . . . , S

t

×

S

w

]

do

11:

p

= [0

,

1

, . . . , S

h

-

1]

T

+

h

flow

[

r

]

▷

∈

R

S

h

12:

Φ

h

[

r

] =

e

j

pf

T

h

▷

∈

C

S

h

×

(

D

h

/

2)

13:

end for

14:

Φ

h

=

reorder

(

Φ

h

)

▷

∈

C

S

t

×

S

h

×

S

w

×

(

D

h

/

2)

15:

f

w

=

θ

-

2[0

,

1

,...,D

w

/

2

-

1]

T

/D

w

16:

for

each

c

in

[0

,

1

, . . . , S

t

×

S

h

]

do

17:

p

= [0

,

1

, . . . , S

w

-

1]

T

+

w

flow

[

c

]

18:

Φ

w

[

c

] =

e

j

pf

T

w

19:

end for

20:

Φ

w

=

reorder

(

Φ

w

)

21:

p

= [0

, . . . , S

t

-

1]

T

22:

f

=

θ

-

2[0

,...,D

t

/

2

-

1]

T

/D

t

23:

Φ

t

=

expand

(

e

j

pf

T

)

24:

Φ

=

flatten

(

concat

(

Φ

t,h,w

)

)

To refine Algorithm 2, we apply a brief optimization on rotary embeddings during early generation steps. Using Eq. (1), we align the generated velocity v θ ( t, x t ) with the target velocity u t ( x ) = σ -1 t ( x t -v ) , where x t is the current latent in time step t , v is latent reference video, and σ is the scheduler sigma.

Fig. 6 illustrates the effectiveness of both optimization and the motion-augmented RoPE initial condition. In Column 1, the subject moves away from the camera, while in Column 2, the subject moves from left to right. The motionaugmented RoPE approach (Columns 3-4) successfully captures the general movements. However, in the second sample, it incorrectly renders the motorbike facing backward. When optimization is performed without a dedicated initial condition (Algorithm 1), the subject placement improves, but issues arise in motion direction (Column 6), and visual artifacts appear (Column 5, Row 2). In contrast, initializing with Algorithm 2 yields the best results across the samples. This approach reduces artifacts, corrects subject orientation and trajectories.

4.3 Phase constraints

Flow matching optimization produces strong results, 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 [47] and analyze the Fourier transform of our signals. Since linear displacements in the spatial domain cause a phase shift in frequency domain,

Input

Algorithm 2

Algorithm 1 + opt.

a Fourier property closely tied to motion transfer, we add a phase constraint to the flow matching objective to guide the model toward more accurate and consistent spatiotemporal alignment.

Specifically, we take the Fourier transform of the target velocity u t along spatio-temporal dimension, to get F ( u t ) = U t = | U t | exp { j ∠ U t } , where | U t | = { Re ( U t ) 2 + Im ( U t ) 2 } 1 / 2 is the magnitude, and ∠ U t = arctan { Im ( U t ) / Re ( U t ) } is the phase. We perform the same transform to the DiT output v θ , and add the phase constraint as a L 1 regularizer to the main optimization objective. We represent the phase on the unit circle ( exp { j ∠ F ( · ) } ) to make them continuous and differentiable everywhere, since the original mapping ∠ F ( · ) contains jump discontinuities at ± π due to being bounded by ( -π, π ] . More clearly, we represent exp { j ∠ F ( · ) } = cos( ∠ F ( · )) + j sin( ∠ F ( · )) and perform the phase-consistency loss in two parts. The final optimization objective is given in Eq. (2):

where λ 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

5 Experimental Results

5.1 Metrics and baselines

We compare our 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-wise 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 Wan2.1-1.3B [38] as the backbone of our method. To obtain a fair assessment, similar to the approach used in DiTFlow [31], we adapt MOFT [42], SMM [45], DitFlow [31] and ConMo [14] to Wan2.1. For the methods that require DDIM inversion [42, 45, 14],

we perform KV-injection from reference video latents similar to [31]. We evaluate GWTF using their CogVideoX-2B [21] checkpoint. The hyper parameters are detailed in Supplementary.

5.2 Fréchet Trajectory Distance

Step 1: Random point sampling

Step 2: Occlusion aware tracking

Step 3: RMS FD over Trajectories

Drop points if they

never become

visible again and

map to Nearest

Neighbor

𝐷𝑖

𝑇𝑟𝑎𝑗𝑒𝑐𝑡𝑜𝑟𝑦 𝑓𝑎𝑘𝑒

𝑇𝑟𝑎𝑗𝑒𝑐𝑡𝑜𝑟𝑦𝑟𝑒𝑎𝑙

Keep partially occluded points

Randomly sample n

points inside and

outside the mask

Discrete Fréchet Distance. Let x i,t ∈ R 2 be the 2D image coordinate of the i th point at frame t ( 1 ≤ t ≤ T ). We denote the reference and generated trajectories by T real i = { x real i, 1 , . . . , x real i,T } and T fake i = { x fake i, 1 , . . . , x fake i,T } , respectively. Then, the discrete Fréchet distance is defined as:

where L is the length of the common re-parameterization, and ( σ, τ ) are non-decreasing index maps that allow each curve to pause or advance but never step backwards. The inner max takes the worst spatial gap along a particular pairing of frames, while the outer min selects the pairing that makes this worst gap as small as possible. Consequently, D F is the minimal worst-case deviation between the trajectories after they are aligned in 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, we uniformly select n points inside the binary foreground mask M 0 , and n outside to capture both object (red) and background (green) motion. Then, the occlusion-aware tracker [24] generates tracks starting with the initial 2 n 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 to 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 i , T fake i } N i =1 . Utilizing the pairs, we calculate root-mean-square Fréchet distance, FTD = ( N -1 Σ N i =1 D 2 F ( T real i , T fake i )) 0 . 5 . For calculating D F , we utilize [10].

Comparison with Motion Fidelity [45]. The Motion Fidelity (MF) metric [45] computes cosine similarity between frame-to-frame displacements on a 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 1, MF also exhibits much higher standard deviation, highlighting its instability compared to FTD.

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 a 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, it reports perfect similarity (MF=1.0) in both cases. This normalization causes MF to ignore motion speed, path magnitude, and spatial alignment, effectively making it translation-invariant but insensitive to geometric deviation. Moreover, MF can be artificially inflated by nearest-neighbor matches anywhere in the frame, as it measures

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=1.414 and 1.0 for Cases 1 and 2, respectively), demonstrating its discriminative power and perceptual robustness.

5.3 Qualitative evaluation

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 Trajectory 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 in spatial offset (Case 2). Although both pairs exhibit substantial geometric deviation, MF remains artificially high because it measures only the directional alignment of motion vectors, discarding scale and positional information. In contrast, FTD penalizes such discrepancies by accounting for the full geometric path similarity, thereby providing a more faithful measure of motion consistency. It is important to note that FTD evaluates distance, hence smaller values mean better motion alignment where MF evaluates direct motion alignment, hence larger values is better.

Fig. 10 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 P1, MOFT, DitFlow, ConMo, and SMM fail to capture the correct motion direction, although SMM maintains proper subject orientation. In P2, some methods struggle with prompt alignment, such as keeping the man stationary, and GWTF introduces noticeable artifacts. For more complex motions like P3 and P4, most methods do not use the reference motion effectively. While GWTF shows motion coherence, it often sacrifices from prompt alignment. For example, it merges a motorcycle with a truck in P3 and does not place the man walking on the wooden dock in P2. A similar issue appears in P6, where GWTF generates a 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. Additional results of our model on challenging videos such as videos with camera motion or multiple subjects can be seen in the Section A.6.

5.4 Quantitative evaluation

Table 1 compares our method with recent motion transfer baselines across five key metrics: 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 ( 0.5816 ) 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 secondbest 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) [31] (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 a 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 2 justifies our approach on motion-augmented RoPE, optimization procedure with flow-matching, and phase constraints. Table 3 elaborates on the selection of first t denoising steps in our optimization, and s number of optimization steps per t . We opted for ( t, s ) = (10 , 5) as we noticed that s = 10 decreases visual quality significantly.

Reference

Ours

GWTF

SMM

MOFT

DitFlow

ConMo

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, users ranked their top-3 videos based on overall visual preference. The results in Table 5 show that our method is consistently preferred across all evaluated dimensions.

These findings reveal two key outcomes: (1) FTD aligns better with human motion perception than MF, and (2) RoPECraft is consistently preferred over all baselines in overall quality.

6 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, flow-matching-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-augmented 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 to controllable video editing.

We discuss limitations and broader impacts in the Supplementary Material.

Acknowledgements. We acknowledge EuroHPC Joint Undertaking for awarding the project ID EHPC-AI-2024A02-031 access to Leonardo at CINECA, Italy. We also acknowledge Fal.ai for granting GPU access.

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Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or 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-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

A Technical Appendices and Supplementary Material

A.1 Ablation on Fourier Features

We ablate the Fourier components for additional constraints on the flow-matching objective on our optimization stage. For the objective min L FM + L c, we ablate two regularizer for magnitude and phase, Eq. (4) and Eq. (5), respectively,

where u t denotes the target velocity, and v θ is the generated velocity output from the transformer at time step t . As shown in Fig. 11, the phase-based constraint proves more effective than the magnitude-based one, supporting the discussion in the main paper. λ is chosen as 1.0 across all experiments, as sweeping λ did not result in significant changes.

Reference

w/Magnitude

w/Phase

A.2 Prompts

We extract prompts from DAVIS [32] videos by using ShareGPT4V [7]. For generating diverse prompts, we utilize LLAMA 3.2 [36]. We construct different system_prompt s for changing the object (1), and environment (2). We also provide a paraphrased prompt for reconstruction (3). The system prompts are listed below:

system_prompt_1 = Answer with a single sentence. You will receive a single-sentence or multi-sentence video prompt. Replace its main subject (the actor or object performing the action) with a 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., a golden retriever a border collie; a sports car a vintage motorcycle). Return only the fully rewritten prompt-no explanations, no 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.

system_prompt_2 = Answer with a single sentence. You will receive a video prompt. Keep the subject(s) and their actions exactly the same, but relocate the scene or setting to a 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_3 = Answer with a single sentence. Do not alter the subject, action, or scene. Simply rephrase the text so it is stylistically

different (synonyms, varied sentence structure) while preserving every factual detail. Return the single paraphrased prompt-no commentary, no headings.

For each original prompt from ShareGPT4V [7], we apply these system prompts to generate its corresponding response. The motion prompts utilized in the main paper figures are listed below:

A sleek, black helicopter is seen around a bustling beach side promenade, passing by a seaside resort building.

A sleek, silver sports car is navigating through the foggy streets of an Italian Renaissance-era town perched on the edge of a rugged cliff overlooking the turquoise Mediterranean Sea.

The video shows a vintage motorcycle driving down a track in a garage.

A woman wearing a beige coat is seen browsing in a bookstore, examining a shelf and then selecting a book from the stack.

A man is seen sprinting across a deserted beach at sunset, his feet pounding against the wet sand.

A 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 of the waves and casts a misty veil over the scene.

A backpacker is seen walking on a rocky terrain with mountains in the background.

A white van is seen driving down a street with a building in the background.

A goose walks on grass and then flies over a river.

A.3 Hyperparameters and Computational Requirements

Hyperparameters. For video synthesis, we adopt Wan2.1-1.3B [38] as the backbone of our method. Since DiTFlow [31] was originally proposed on CogVideoX [21], and MOFT [42], SMM [45], and ConMo [14] were developed on UNet-based architectures, we re-implemented all these baselines using the same Wan2.1-1.3B backbone for a 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.

We conducted extensive experiments to determine optimal hyperparameters for each method in the 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 of 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 β parameters.

0

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, we used the original CogVideoX weights provided by the authors for GWTF [4], as training the full Wan2.1 pipeline from scratch was infeasible. To align more closely with Wan's 1.3B parameter scale during evaluation, we used the 2B checkpoint of GWTF.

Computational Requirements. We run all the models on a shared cluster, with compute nodes equipped with 4 × NVIDIA A100 64GB.

A.4 Fréchet Trajectory Distance

We provide our Fréchet Trajectory Distance pseudocode in Listing 1, where the frechetdist is calculated by using [10] and cotracker3 by [24].

Listing 1 Fréchet Trajectory Distance implementation.

A.5 Limitations and Broader Impacts

Limitations. We present the limitations of our 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-augmented rotary embedding generation.

A limitation of our proposed Fréchet Trajectory Distance is its reliance on CoTracker3 [24], which has difficulty handling zoom in or zoom out camera motions, especially in cases where the tracking points remain stationary. This limits the accuracy of the extracted trajectories in such scenarios.

Broader Impacts. The ability to generate realistic motion in videos can greatly benefit fields such as animation, virtual production, education, and accessibility. However, it also introduces risks, particularly in the creation of deepfakes and other forms of synthetic media that may be used

P1: A person is seen water skiing in the ocean, being pulled by a boat.

Figure 12: Limitations of our method. In the first video, a boat (highlighted with a red rectangle) intermittently appears and disappears, likely due to 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 a 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 of 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 a randomly sampled subsample from RealCamVid [49], each containing camera motion and multiple objects. Table 6 reveals that our method is also robust on more complex datasets than DAVIS.

P8: The video depicts a team of sled dogs pulling a musher across the snow-covered ground.

Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
**Strengths**
- Novelty. The authors propose a novel motion transfer method that leverages optical flow to warp rotary positional embeddings.
- Competitive results. The proposed method achieves competitive performance in both qualitative and quantitative evaluations.
- Clarity. The paper is clearly written and well-organized.

**Weaknesses**

- 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.
- 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.

QUESTIONS:
Please refer to the weaknesses for my questions on this paper.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-26

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nQUALITY\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\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\n- Limitations are adequately discussed", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-28"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper is well written and easy to follow.

2. The idea of modifying rotary positional embeddings (RoPE) for motion transfer is insightful and interesting.

Questions:

1. All showcased examples and supplementary results focus on single-object motion transfer. Can RoPECraft handle multiple-object motion transfer?

2. 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?

3. The column indices referenced in Figure 2’s caption appear misleading—please correct them in the final version.

QUESTIONS:
N.A.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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?\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nThis paper relies on a pre-trained optical flow estimator. If the optical flow extracted is not accurate enough, the proposed method will likely fail.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}