MTVCraft: Tokenizing 4D Motion for Arbitrary Character Animation
Authors: Yanbo Ding
Xirui Hu
Guo Zhi Zhi
Yan Zhang
Xinrui Wang
Zhixiang He
Chi Zhang
Yali Wang
Xuelong Li
Venue: ICLR 2026 Poster (2026)
Keywords: Character Animation
Motion Tokenization
Video Generation
Primary area: generative models
TL;DR: We propose MTVCraft, a novel paradigm for animating arbitrary characters with 4D motion tokens.

Abstract: Character image animation has rapidly advanced with the rise of digital humans. However, existing methods rely largely on 2D-rendered pose images for motion guidance, which limits generalization and discards essential 4D information for open-world animation. To address this, we propose MTVCraft (Motion Tokenization Video Crafter), the first framework that directly models raw 3D motion sequences (i.e., 4D motion) for character image animation. Specifically, we introduce 4DMoT (4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens. Compared to 2D-rendered pose images, 4D motion tokens offer more robust spatial-temporal cues and avoid strict pixel-level alignment between pose images and the character, enabling more flexible and disentangled control. Next, we introduce MV-DiT (Motion-aware Video DiT). By designing unique motion attention with 4D positional encodings, MV-DiT can effectively leverage motion tokens as 4D compact yet expressive context for character image animation in the complex 4D world. We implement MTVCraft on both CogVideoX-5B (small scale) and Wan-2.1-14B (large scale), demonstrating that our framework is easily scalable and can be applied to models of varying sizes. Experiments on the TikTok and Fashion benchmarks demonstrate our state-of-the-art performance. Moreover, powered by robust motion tokens, MTVCraft showcases unparalleled zero-shot generalization. It can animate arbitrary characters in full-body and half-body forms, and even non-human objects across diverse styles and scenarios. Hence, it marks a significant step forward in this field and opens a new direction for pose-guided video generation. Our project page is available at https://github.com/DINGYANB/MTVCrafter. A scaled version has been commercially deployed and is available at https://telestudio.teleagi.cn/generatevideo/creativeWorkshop.

Published as a conference paper at ICLR 2026
MRVCRAFT: TOKENIZING 4D MOTION FOR ARBITRARY CHARACTER ANIMATION
Yanbo Dingl.244, Xirui Hu Zhizhi Guo Yan Zhang', Xinrui Wang?, Zhixiang Hc², Chi Zhang", Yali Wang1.5., Xuclong Li2.1
Shenzhen Key Laboratory of Computer Vision and Pattern Recognition, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2 Institute of Artificial Intelligence (TeleAI), China Telecom, Beijing, China
á School of Computer Science and Technology, Xi an Jiaotong University, Xi'an, China
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
SShanghai Artificial Intelligence Laboratory, Shanghai, China
{yb. ding, yl. wang ssiatssitt...... .c..c,,
xiruihuêstu. xjtu. edu. en, guozz2@chinatelccomm.
ABSTRACT
Character image animation has rapidly advanced with the rise of digital humans. However, existing methods rely largely on 2D-rendered pose images for motion guidance, which limits generalization and discards essential 4D information for open-world animation. To address this, we propose MTVCraft (Motion Tok- enization Video Crafter), the first framework that directly models raw 3D mo- tion sequences (i.e., 4D motion) for character image animation. Specifically, we introduce 4DMoT (4D motion tokenizer) to quantize 3D motion sequences into 4D motion tokens. Compared to 2D-rendered pose images, 4D motion tokens offer more robust tween pose images and the character, enabling more flexible and disentangled control. Next, we introduce MV-DiT (Motion-aware Vidco DiT). By design- ing unique motion attention with 4D positional encodings, MV-DiT can effec- tively leverage motion tokens 4D compact yet expressive context for character image animation in the complex 4D world. We implement MTVCraft on both Cog VideoX-5B (small scale) and Wan-2.1-14B (large scale), demonstrating that our framework easily scalable and can be applied to models of varying sizes. Experiments on the TikTok and Fashion benchmarks demonstrate our state-of- the-art performance. Moreover, powered by robust motion tokens, MTVCraft showcases unparalleled zero-shot generalization. It can animate arbitrary char- acters in full-body and half-body forms, and even non-human objects across di- verse styles and scenarios. Hence, marks significant step forward this field and opens a new direction for pose-guided video generation. Our project COto//DINGYAAEEE /MVVCafferreeee
cues and avoid strict pixel-level alignment be-
page is available at lhttps:/ scaled version has been commercially deployed and is available at https: // orgereaaa....aaaaa.aaaaaaaaaaaar
INTRODUCTION
Character image animation (Chang et al. 2025 2023b Xu et al.) 2025b Men et al. 2024). which aims synthesize videos of reference character image driven by pose sequences estimated from an
These authors contributed equally, and this work was done during internship at TeleAI.
Corresponding Authors. Project Leader.

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Published as a conference paper at ICLR 2026
ControiNc
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Tirget
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ControlNeXr
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Targel
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ControlNeXi
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Figure 1: Teaser. propose MTVCraft, a versatile framework that can effectively transfer pose sequences from driven video in either full-body half-body settings, while supporting wide range of visual styles such as anime, pixel art, ink drawings, and photorealism. Beyond human characters. MTVCraft is further capable of handling non-human subjects such animals and even inanimate objects, demonstrating superior robustness, strong generalizability to open-world scenar- ios, and the emergent ability to animate arbitrary characters.
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Published as a conference paper at ICLR 2026
They are fuoddnwastllly 2P pixel- Level images (12v)
What of we dirroblyy toleevizze 4D mobion (MzzV)
Driven Video
Reference Image
Driven Video
Reference Image
2D Image Encoder
2D Image
Encader
Depth Images
Mesh Images
SMPL Joints
Images
Motion Tokenizer
fixed shape
missing 3D
lack of details
Encodke
information
Oulput
L,
Output
(a) Existing methods use 2D rendered images for pose guidance.
(b) Ours directly uses 4D motion tokens for pose guidance.
Figure 2: Motivation. Directly tokenizing 4D motion captures more faithful and expressive infor- mation than traditional 2D-rendered pose images derived from the driving video.
input video, has attracted increasing attention due to its broad applications in digital humans (Lauer-
Schmaltz et al., 2024; Hu et al.] (2025)), virtual try-on (Islam et al.] 2024 Song et al. 2024), and
immersive content creation (Chamola et al. 2024; Qin & Huil 2023) ). To meet the growing demand,
numerous methods (Hu) 2024 Hu et al. 2025at Tu et al. 2024] Zhu et al.
2024b; Xu et al. 2024
Gan et al. 2025 Zhang et al. 2024a Peng et al.) 2024] Wang et al. 2025 Zhou et al. 2025 have
been proposed to achieve high-quality animation with realistic motion and consistent appearance.
However, as shown in Figure existing methods depend on 2D-rendered pose images to provide motion guidance for the generative model. This introduces two fundamental limitations. First, al- though pose images provide basic structural cues, they inevitably discard rich spatial-temporal mo- tion from the real 4D world. Hence, they struggle to synthesize physically plausible and expressive motions, especially in complex 4D scenarios (c.g.. Gymnast in Figure 13 Second, when the pose is provided in images, the model tends to blindly copy the fixed-shaped poses pixel-by-pixc without grasping the underlying motion semantics. Consequently, the animation often exhibits distortions or artifacts, especially when the pose images from the driving video significantly deviate from the reference appearance in shape or position (e.g.. Hulk in Figure Z Hence, a natural question arises: can we directly model raw 4D motion rather than 2D-rendered pose images for animation?
To answer this question, we draw inspiration from recent advances in motion generation (Hosseyni et al. [2025] Guo et al. 2024: Jiang et al.) 2023) Zhang et al. (023a),, where SMPI. body parameters (Plappert et al. 2016 Guo et al.] 2022) are first quantized and subsequently used for motion genera- tion. Built upon this insight,
propose MTVCraft (Motion Tokenization Video Crafter), novel framework that combines a 4D motion tokenizer with a motion-aware video Diffusion Transformer
(Zhu et al. 2024a Kong et al. 2024 Zheng al. 2024) for arbitrary character image animation. Firstly, to leverage richer spatial-tempont information in the 4D world than what can be captured by 2D image renderings, we propose 4DMoT (4D Motion Tokenizer) to directly quantize 4D human motion data (i.e., 3D joint coordinates over time). The resulting motion tokens faithfully preserve the information of raw motion. effectively addressing the first limitation of lacking explicit 4D infor- mation. Secondly, we propose MV-DIT (Motion-aware Video DiT) for controllable animation. By integrating 4D motion attention into DIT blocks, our MV-DiT effectively leverages motion tokens as context for vision tokens. This design eliminates the need to render pose images and enables the model to better learn underlying motion semantics, thereby addressing the second limitation of pixel-level copying. To further improve spatial-temporal modeling, we incorporate unique 4D po- sitional encodings (1D temporal + 3D spatial) into the motion attention. With this unified design, MTVCraft can be easily applied to different model scales, from CogVideoX-5B (Yang et al. (024b) to 18B on Wan-2. 1-14B (Wan et al. (025). Leveraging 4D motion tokenization and motion atten- tion, MTVCraff establishes new paradigm for character animation and demonstrates versatility.
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Published as a conference paper at ICLR 2026
Our contributions summarized follows: (1) We introduce MTVCraft, the first pipeline that directly models raw 4D motion instead of 2D-rendered pose images for arbitrary character image animation. (2) We introduce 4DMo'. novel motion tokenizer that encodes joint coordinates into 4D compact yet expressive tokens, providing more robust spatial-temporal guidance than 21) pose image representations. (3) We design MV-DiT, motion-aware video DiT model equipped with unique 4D motion attention and 4D positional encodings, enabling animation effectivveyy guided by 4D motion tokens. We implement two versions of MV-DiT, corresponding to small and large model scales. (4) MTVCraft achieves state-of-the tf-tee-r-t- performance on the TikTok (Jafarian & Park 2021) and Fashion (Zablotskaia et al.) 2019) benchmarks. Moreover, as shown in Figure [ MTVCraft showcases powerful zero-shot generalization to unseen motions, styles, scenarios, and characters, including full-body or half-body, and even non-human objects.
2 RELATED WORK
Diffusion Models for Controllable Generation Diffusion Models (Sohl-Dickstein et al. 2015
Ho et al. 2020, Song et al. 2020) have achieved remarkable success in visual generation. Unlike traditional GAN- -ased methods (Lee & Seok 2019 Xu et al. 2018 Goodfellow et al. (020 which
often suffer from training instability and mode collapse, diffusion-based approaches offer more sta- ble training dynamics and can generate high-quality content with improved diversity. This superior performance has led Stable Diffusion series (Rombach et al.. 2022, Esser et al., 2024 Podell et al., 2 quickly dominate the field of vision- seiinngenerative Al. Some other works (An al., 2026 Liang et al. (0266) systematically survey recent advances. To enable finer control beyond text, Con- trolNet (Zhang et al., 2023b) uses grr-initializationn and network duplication to guide structural elements such as sketches and depth maps. ControlNeXt (Peng et al. (024) improves this design with a lightweight module and cross-normalization strategy. Animate (Guo et al., 2023) intro- duces motion control by injecting temporal layers into text-to-video diffusion models. Other spe- cialized methods extend controllability to aspects such as motion trajectories (Zhang et al. 2024b] Xiao et al. 2024), camera viewpoints (Hou et al.) 2024 He al. 22024,, and scene layouts (Ding et al. 2024 Feng et al., 2023). In this work, we address the challenging task of arbitrary character animation, with the goal of achieving precise pose control across diverse characters in the 41) world.
Character Image Animation Early approaches (Huang et al. 2021 Siarohin et al. 2021 2019
al. 2019) predominantly adopt GANs to animate the reference image, but struggled with visual artifacts. Recent advances in diffusion models have inspired their application to character image an- imation (Hu) 2024 Luo et al.
2025 Zhou et al., 2025). Disco (Wang et al., 2024a) first introduces hybrid diffusion architecture with disentangled control over human foreground, background, and pose. MagicAnimate (Xu et al. (024) and Animate/Ayonee (Hu) (024) developed specialized refer-
ence and pose networks to control appearance and motion, respectively. Champ (Zhu et al. (024bb) leverages mesh renderings for enhanced controllability, while Unianimate (Wang et al., 2024b) and Unianimate-Dii DiT (Wang et al., 2025) integrates Mamba (Gu & Dao 2023) into diffusion models for
improved efficiency. MimicMotion (Zhang et al. (022a) and Realiscance (Zhou et al. (024) imple- ment regional loss functions to mitigate distortion. StableAnimator (Tu et (0224) uses based
(Peng 1992 Bardi et al.) 1997) optimization enhance identity preservation. AnimateX (Tan al. 2024) and AnimateAAyone-2 (Hu et al.; (025a) extend motion transfer to non-human subjects and environmental interactions, respectively. Human-DiT (Gan et al. 2025) and HyperMotion (Xu al.
2025a) use DiTs to enhance the animation quality and temporal coherence.. Importantly, all these methods rely on 2D images for pose guidance, including skeletons (e.g. DWPose (Yang al. (023) and OpenPose (Cao et al. 2019)). SMPL (Pavlakos et al.) 2019 Loper et al., (023) renderings, or depth maps (Yang et al. 2024a)). Our work directly encode 4D joint coordinates without interme- diate rendering, and designs motion attention in DiT's to effectively leverage 4D motion tokens.
METHOD
Diffusion Transformer The Diffusson Transformer (DiT) (Kong et al., 2024 Zheng et al. 2024 Yang al. 2024b), a prevailing approach. integrates Transformer basedd based backbone into the diffusion process. Using Patchify (Peebles & Xie (203) and Rotary Positional Encoding (RoPE) (Su et al. 2024), the denoising network can effectively process inputs with varying spatial and temporal
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Codebook
v
code
height; frame num
E
k F 1/4
Encoder
4D Motion
E
Quantizer
C
Decoder
C. 3)
flatten
reshape
Motion
Input SMPI. Joints
Reconstructed
SMPL Joints
7.
Convolution
ResNet Block
sliding
Down Sampling
Convolution Out
width: joint num (j)
space
Figure 3: Architecture 4DMoT. An encoder-decoder framework learns spatial-temporal latent representations of SMPL joint coordinates, and vector quantizer learns 4D compact yet expressive tokens in a unified space. All operations are in 2D space along the frame and joint axes.
dimensions, thus improving scalability and adaptability compared with U-Net (Ronneberger et al., 2013). In practice, RoPE encodes relative positional information via rotation in complex space:
R2 m)
(oo)) sin(m0.)
sin(m):)) cos(mo,)
22
#26+1
where is the input query or key vectors, is the positional index, i is the feature dimensional index. 6g is the frequency, i.e., 10000-22/10 and D is the dimension of the attention layer.
Overview After the preliminary introduction, we next explain our MTVCraft in detail. Section 3.1 we introduce 4DMoT for motion tokenization, where the resulting 4D motion tokens provide more robust
cues than 2D-rendered pose images. In Section 3.2 we present MV- DiT animate characters conditioned on 4D motion tokens, featuring motion attention with unique positional encodings and motion-aware classifier-fre guidance (Ho & Salimans] (002). Finally, in Section 3.3 show that method is easily scalable to larger model sizes with only minor architectural adjustments, demonstrating its flexibility for practical deployment.
3.1
4D MOTION TOKENIZER
To guide character animation with rich 4D signals, we extract SMPL Looper et al. 2023 sequences from the driving video as conditions. Prior works (Zhou et al. 2024 Pang et al. 2024 Zhu et al.) 2024b) render 3D meshes into 2D images, often yielding deficient motion representation and intro- ducing shape and position biases (Figure 2 In contrast, directly tokenize raw SMPL sequences into 4D motion tokens, decoupling motion from absolute position and shape variations to obtain a compact, robust representation. We first curate SMPL motion-video training dataset, then design a 4D motion VQVAE (Figure to learn noise-free tokens for subsequent animation.
Motion-Video Dataset Preparation Existing open-source datasets like (Jafarian & Park 2021 Zablotskaia et al. 2019). are limited in both motion diversity and visual quality, which constrains their effectiveness in training powerful generative models. To this end, we curated a diverse dataset comprising 200K video clips. These clips are sourced from public datasets and web-crawled content videos, covering a wide variety of human figures. The clips are then rigorously filtered to ensure high-quality (see Appendix B). For each remaining clip, 2024 to estimate SMPL rotations (0 and root translations {Tr)T=1- where is the number of frames. The estimated 0, are combined with standard human SMPL shape to compute 3D joint coordinates J₁ E 24x3 using forward kinematics (Loper et al. 2023). Here, we use a standard neutral SMPL shape instead of the per-frame predicted
NLF-Pose (Sárándi & Pons-Moll]
decouple motion from individual
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Published as a conference paper at ICLR 2026
shape variations. Furthermore, the subsequent tokenization enhances this decoupling by learning motion representations independently of shape, allowing the model to focus on motion dynamics. then added to Jr to preserve position changes over time. The resulting
The root translations Jr serves as input to 4DMo'T, The final training dataset comprises 30K high-quality SMPI. motion- video pairs, averaging 600 frames per video and covering diverse motions and scenarios.
Model Architecture of our 4DMoT Since the VQVAE is widely used for discrete tokenization
in downstream tasks (Guo al. 2024 Wang al., 2024c Ma et al. 2025), we build upon its ar- chitecture. As shown in Figure our 4DMoT consists of an encoder-decoder for reconstructing 4D joint coordinates, and quantizer for learning discrete motion tokens. The encoder-decoder captures -emporall information, while the quantizer helps to remove noise and learn compact repre- sentation in unified space. Specifically, given a motion sequence (J1, and joints, we first normalize it using global statistical mean and standard deviation of the dataset, and then convert it into a relative representation M by subtracting the first frame, so that the first frame has all-zero joint coordinates. All subsequent processes based on these differential joint coordinates. In this way, we enable the model to learn relative motion patterns and thereby decouple motion information from absolute positions. This approach leads to more robust and disentangled spatial-temporal representations compared to traditional mesh renderings. The encoder then maps it into a continuous latent space via residual blocks with 2D convolutions along temporal (/) and spa- tial (i) dimensions, and downsampling blocks with average pooling. This design enables effective interactions across both time and joint dimensions simultancously and yields latent representations
sequen
Jr} with f frames
{Fm E [Rd] ]= (1+(f-1)//A)x] (first frame not downsampled, denotes the token dimension). Next, a vector quantizer discretizes E via nearest-neighhor lookup in a learnable codebook {C,, E )== where denotes the codebook size. The codebook is optimized with Exponential Moving Aver- age (EMA) (Polyak & Juditsky| 1992) and codebook resetting technique (Hosseyni et al.) (025) to maintain usage diversity. Finally, the decoder, which has similar structure to the encoder but with upsampling blocks, reconstructs the differential motion M from the quantized codes C. Moreover, to better capture long-range dependencies, we incorporate dilated convolutions and a sliding win- dow strategy. The complete training objective combines a reconstruction loss with commitment loss to ensure faithful reconstruction and codebook effectiveness:
Lvq
| M
||| +B E sg [C]]
reconstruction
commitment
Where sg[-] denotes the stop- stop-gradient operation, ,3 is hyperparameter to control the weight of the commitment loss, and C are the latents before and after quantization, respectively.
Tokenization Strategy: Coordinates vs. SMPL Parameters Unlike prior motion generation works that tokenize SMPL parameters (c.g., (Guo et al. 2024)), we opt to tokenize SMPL joint co- ordinates for two reasons. First, our task focuses video generation rather than predicting SMPL parameters. Joint coordinates capture spatial continuity and provide explicit positional information directly aligned with pixel-level generation, making them better suited than SMPL parameters that encode rotations indirectly. Joint coordinates can also be naturally expressed a differential form relative to the first frame, allowing the model better learn motion dynamics while decoupling them from absolute positions. Second, tokenizing coordinates avoids potential instabilities and ambigui- ties inherent in SMPL rotation representations (e.g., axis-angle discontinuities). While prior works explored SMPL-parameter tokenization for motion generation, directly using such representations for video generation may not provide interpretable control. In practice, differential joint coordinates provide a more stable and robust representation, facilitating effective learning in 4D space.
3.2
4D MOTION VIDEO DIFFUSION TRANSFORMER
With compact and noise-free 4D motion tokens obtained from 4DMoT,, we next leverage them to drive character image animation in MV-DiT. In this section, describe how these motion tokens are incorporated as conditioning signals, including identity preservation, 4D positional encodings, 4D motion attention, motion-aware classifier-free guidance, and scaling to larger model sizes.
Identity Preservation Maintaining visual consistency of the reference character image is crit- ical for controllable animation. A common strategy in previous methods (Chang et al.) 2023b

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Published as a conference paper at ICLR 2026
4D Motion Tokenizer
2 restion
Z
4D Motion Attention
4D Positional Encoding
niffiyere
RePE
randonly replace
linear
Scoled Arrensooe
espand
Out Ph Propsttonn
SMPL
X: ID KAPE
morion ectration
inconitiional melion tokerss
nulfeaue espand
nam
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el applaane to Rature
video seqpence
norise
4D Mution Attn
RoPE
Scale & Staft
3D Fall
Scale & Shift
Food Forward
VAE
Concatenate
repeal
Gole
Gate
Frevan
Trainable
Reference Image
block
Figure 4: Architecture of MTVCraft-6BB Based on the video DiT model, design unique 4D motion attention to leverage 4D motion tokens as context for vision generation. To enhance spatial- temporal relationships, we apply 4D RoPE over (t, x, y, z) coordinates attention keys and queries.
Zhou et al. 2024 Chang et al. 2023a 2024 Xu et al. 2024) is to introduce a separate reference network model identity appearance independently. While effective, such designs in- crease architectural complexity and computational overhead. In contrast, we adopt more straight- scheme. Specifically, given the noisy video latents
forward yet effective seeeattand-concateenn { E /xexhxx and reference image latent žrer E Rexxxw obtained from frozen shared VAE encoder, we compute the composite vision latents following:
Concat (20, Repeat(Zedt f)) E R x2xxxxx
Zvision
This design explicitly injects identity information at every frame. Thanks the 3D full self-attention mechanism in DiT, the model can directly establish interactions between video latents and reference image latents across spatial-temporal dimensions. As result, identity consistency is preserved with- out introducing additional reference branches, leading to a simpler and more unified architecture.
4D Positional Encodings To further enhance ppatial-temport relationships and enable more meaningful interaction between motion and vision tokens, we extend the standard 3D RoPE of vision tokens into 4D space and introduce dedicated 4D RoPE for motion tokens.
Standard:
P3D = Concat( Rs, R₁, Rew)
(4)
PaD Concat( (R, Rg Ryy R.y
where each R. implements 1D rotary embeddings (Su et al. 2024) along a specific coordinate axis and is broadcast across other dimensions. The motivation for extending 4D is two-fold. First, motion tokens naturally reside in structured 3D space evolving over time. Second, a unified posi- tional formulation allows motion and vision tokens to share compatible geometric semantics during attention. As shown in Figure 4 for motion tokens 2motion. we compute positional encodings based on coordinates (t, x. y, 2), where t denotes the frame index, and (z. y, 2) are the mean joint posi- tions averaged over all frames, representing 24 joints in 3D space. Using dataset-averaged positions provides unified reference for different human poses, offering stable and consistent positional cues that facilitate faster training convergence. Meanwhile, for vision tokens 2vision which lack depth axis, we use (t, h, w) and assign = 0 for the rotary embeddings along the depth dimension. This preserves the original 3D RoPE behavior while ensuring positional compatibility with motion tokens. With this 4D formulation, both motion and vision tokens can interact coherently within subsequent attention layers. Further implementation details are provided in Appendix F
4D Motion Attention
To effectively leverage motion tokens 2mation as context for vision tokens Evisions introduce 4D motion attention shown in Figure 4 where Zyision as queries and

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Published as a conference paper at ICLR 2026
Emotion serve keys and values, enabling the model to dynamically retrieve motion cues when gen- video representations. The attention mechanism formulated as follows:
erating
Q RoPE(LayerNorm/ (2(vissioa)) Pabiion)
(5)
K = RoPE(LayerNorm W (Zmotion)) P
(6)
V LayerNorm(W... (2motion)) )),
QK vdk
Attention( Q. K, V) Softmax
V.
Here, W. W5, and W. E Rdxd learnable projection matrices, and Piiiion and Pmotion denote the 4D RoPE applied to vision and motion tokens, respectively. The RoPE formulation follows Equation The attention output is added Avision via residual connection, enabling motion-aware modulation while preserving spatial-temporal consistency of the video latents.
Motion-awasd aware Classifier-free Guidance To further enhance generation quality and generaliza- tion, we extend classifier-free guidance (CFG) to motion tokens. Standard CFG interpolates between conditional and unconditional predictions éa ce(2e, c2) + w w(Ep(2a (2t, fe,,t,c ct) eo (Z,, cæ) cg)), where Eg is the denoising network, 2, is the noisy latent at timestep t. ct is the condition, and w con- trols the guidance strength. However, motion tokens lack natural unconditional form. We therefore introduce learnable unconditional motion tokens Cmo@. During training, Cr is randomly replaced by Cmoz with predefined probability (i.e., CmoB is only updated when used). This enables joint learn- ing of conditional and unconditional generation, enhancing robustness and controllability.
3.3 MODEL SCALING
Following the scaling law of generative models (Wang 2025 Zhou et al., 2025 Cheng al.,
2025). we scale MTVCraft from 6B to 18B parameters. The 6B version uses Cog Video (Yang al. 2024b) as the backbone, while the 18B version adopts Wan-2-1-141 (Wan et 2025). which provides significantly stronger visual generation capacity and is therefore better suited for animating diverse open-world characters. As shown in Figure (6, the scaled version introduces an additional
text control branch compared with the 6B version in Figure E thus enabling joint control by both text and motion. We reuse 4DMoT and unconditional motion tokens without modification, which substantially reduces training cost. For 4D motion attention, since the motion tokens derived from codebook are of dimension 3072 while the vision tokens in Wan-2-1 have dimension 5120, we apply zero-padee eero-padding along the channel dimension for alignment. This preserves the spatial-tempontt struc- ture of motion tokens faithfully without requiring additional transformations or retraining. Overall, MT'V Craft demonstrates strong scalability and seamless integration of 4D motion tokens, indicating that our method is readily applicable to and versatile across different diffusion backbones practice. More architectural details of MTVCraft-188D are provided in Appendix C
4 EXPERIMENT
conduct experiments on TikTok (Jafarian & Park 2021) and Fashion (Zablotskaia et al. 2019) benchmarks. The evaluation metrics are detailed in Appendix[] And the evaluation details are provided in Appendix A.3
Benchmarks
Following (Zhou et al. 2025
Implementation Details For 4DMoT, we use codebook of size 8192 with a code dimension of 3072. Quantization is performed with an exponential moving average update strategy using decay constant of X = 0.99. To maintain codebook utilization, unused codes are reset every 20 steps. The sliding window size is set to 8, and the commitment loss weight in Equation 2 is set to 0.25. For MV-DiT, we insert a 4D motion attention module every two DiT blocks. The condition drop probabilities are set to 0.2 for motion and 0.1 for text, respectively. The classifier-free guidance scale is set to 3.0 for motion conditions and 6.0 for text conditions. All experiments are conducted on NVIDIA H100 GPUs. Additional training and architectural details are provided in Appendix A
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Table 1: Quantitative Results TikTok
Jafarian Park
2021
Benchmark.
Model
PSNR
SSIM
LPIPS
FID
FVD1
FID-VID
MuscPose
Hu
2024
18.20
0.757
0.248
41.99
532.75
14.60
MooraAA
2024)
18.62
0.764
0.230
37.28
501.22
12.40
ControlNeXI P eng
2024
16.31
0.728
0.296
33.48
548.01
28.23
Animate-X Tan ct al.
2024)
16.71
0.743
0.285
32.77
508.63
17.47
MimicMotion (Zhang
2024a)
19.30
0.751
0.220
34.88
472.51
9.30
RealisDance-Dil (Zhou et al.
2025
17.55
0.717
0.261
30.39
458.81
Unianimate-DiT (Wang al.
2025
19.35
0.765
0.235
28.47
402.14
9.12
MTVCrafi-6B MTVCraft-I8B
19.35
0.760
0.219
23.58
317.21
8.56
19.84
0.779
0.217
20.70
276.65
7.31
Reference
Ours
UniAnimate-DiT
AnimateX
ControlNeXt
Qualitative Comparison. Our MTVCraft consistently demonstrates the best motion transfer performance and high appearance consistency across various scenes and diverse characters.
Figure 5:
4.1 SOTA COMPARISON
Figure ! 13 15 demonstrate MTVCraft's superior animation performance pose accuracy and identity consistency, MTVCraft exhibits strong zero-shot generalization in 4D worlds, handling full- body or half-body characters, across diverse styles and scenes. MTVCraft remains robust when the target pose is misaligned with the reference image (e.g., Owl in (5. while other methods fail, highlighting effective disentanglement of motion from the driving video. Importantly, despite be- ing trained exclusively human-centric dataset, MTVCraft is capable of animating non-human subjects, including animals and inanimate objects. This demonstrates the benefit 4D motion representation via tokenization of differential joint coordinates. Table shows state-of-the-ar tof-the-rrt per- formance across all metrics, and additional experiments Appendix further confirm it. More qualitative comparisons and cases provided in Appendix N

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Published as a conference paper at ICLR 2026
Table 2: Ablation Study TikTok (Jafarian & Park
2021
Benchmark.
Model
Choice
PSNRT
SSIM
LPIPSI
FID4
FVD1
FID-VIDU
w/o quantize
18.76
0.732
0.226
24.04
332.97
9.39
4D MT
w/o differential motion
19.08
0.740
0.223
24.37
325.40
8,92
w/ 3D quantization
19.12
0.742
0.221
23.94
329.86
9.04
w/ dynamic
17.23
0.733
0.247
28.24
383.22
11.85
w/ learnahle PE
16.90
0.719
0.259
28.69
397.64
11.74
4D MA
w/ ID temporal RoPE
16.86
0.718
0.263
29.45
458.29
12.15
w/ 2D spatial RoPE
16.28
0.704
0.266
30.07
459.59
10.73
w/ 3D spatial RoPE
16.99
0.723
0,259
28.15
435.80
11.28
w/o PE
16.51
0.707
0.281
32.56
548.31
13.40
Our Default Design
19.35
0.760
0.219
23.58
317.21
8.56
4.2 ABLATION STUDY
To validate our key designs, conduct ablation studies on the 4D Motion Tokenizer and 4D Motion Attention. Table 2 shows the performance impact of modifying or removing specific components.
4D Motion Tokenizer (MT) (1) Without quantization, the VQ-VAE degenerates into standard autoencoder producing continuous motion tokens, leading to degraded performance. This confirms that discrete motion tokens in a unified space help stabilize motion learning and improve general- ization across diverse motion patterns. (2) Removing differential motion also worsens performance, as it prevents the model from explicitly modeling relative joint displacements, which are crucial for capturing fine-ge -ee-ggra and temporally coherent motion dynamics across frames. (3) Compared with 3D quantization (t, x, y), adding the 7-axis captures depth- geometric information and further improves overall results. We provide more systematic analysis of our 4DMoT in Appendix
4D Motion Attention (MA) We explore various positional encoding (PE) designs for the motion attention module: (1) Dynamic PE computes RoPE using joint coordinates of the first frame, but per- forms poorly due to instability and training difficulties; (2) Learnable PE struggles to converge and fails to provide reliable positional cues; (3) 1D temporal RoPE, (4) 2D spatial RoPE (x, y). (5) 3D spatial RoPE (x, y, z) all fail to capture full 4D spatiotemporal dependencies, resulting in noticeable visual artifacts such as identity drift or temporal jittering: (6) w/o PE completely removes posi- tional encoding and yields the worst performance (e.g., FVD 548.31 versus 317.21). highlighting the necessity of explicit positional information. Overall, these results demonstrate the importance of modeling 4D positional information. Additional ablation studies are provided in Appendix H
5 CONCLUSION
We present MTVCraft, the first framework that directly tokenizes raw motion sequences for control- lable character video generation. By integrating 4D motion VQVAE with novel motion attention and 4D RoPF, MTVCraft enables precise 4D controllability and achieves state-of-the-art general- ization to arbitrary characters, including non-human and Al-generated ones. Finally, its scalability to larger model sizes further enhances -eriialltemoral coherence, identity fidelity, and controllable animation quality, paving the way for more versatile and realistic character image animation.
ACKNOWLEDGGEENTS
This work was supported by Guangdong Science and Technology Program (Grant No. 2024TQ08X365).
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Appendix
TABLE OF CONTENTS
More Implementation Details
18
A.1 Model Architectural Details
18
A.2 Training Details
18
A.3 Evaluation Details
18
More Details of Dataset Curation
18
More Details of Model Scaling
20
D
Limitation and Discussion
20
More Details of 4D Motion Tokenizer
21
E.1
Tokenizing Strategy: Discrete vs. Continuous
Tokenization vs. Rendering Images
22
E.3 Analysis of Codebook
22
Reconstruction Performance
22
More Details of 4D Motion RoPE
22
F11
4D) RoPE Design and Implementation
23
Visualization and Analysis of 4D RoPE
23
More Quantitative Experiments
24
H More Ablation Study
24
Training Curves
24
Evaluation Metrics
25
Ethics Statement
27
Reproducibility Statement
27
M Use of Large Language Models
27
N More Qualitative Comparisons
27
0 More Visualization Results
27
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A MORE IMPLEMENTATION DETAILS
A.1 MODEL ARCHITECTURAL DETAILS
4DMoT Our 4D motion tokenizer consists of an encoder, a quantizer, and a decoder. Encoder begins with a convolutional input layer that projects the input channels from 32, followed by three ResNet blocks and downsampling blocks, with channel dimensions of (32, 128, 512), frame downsampling rates (2. 2, 1), and joint downsampling rates (1, 1, 1). A final convolutional output layer maps the features to a code dimension of 3072. Quantizer uses codebook of size 8,192 with a code dimension of 3072. Decoder starts with a convolutional input layer that projects the code dimension from 3072 to 512, followed by three ResNet blocks and upsampling blocks symmetric to those in the encoder. A final convolutional output layer maps the features back to 3 channels. The overall number of trainable parameters in our 4DMoT is 48M.
MV-DIT The 6B version is based on CogVideoX: --B-TXX (Yang et al. 2024b). To better support motion-centric generation, we remove the original text- sext-processing branch and insert our proposed 4D Motion Attention layer after the self-attention layer in every two Cog VideoX blocks. We train all the DiT blocks since the input channels are revised. The total number of trainable parameters in this small-scale model is approximately 6B, with all attention parameters randomly initialized. The 18B version is based on Wan-2-1-12v-144
(Wan et al.) 2025). In this version, the text-processing branch is retained, and the 4D Motion Attention layer is inserted after the cross-attention layer in every two Wan-2-1 blocks. We only train the newly added attention modules to better preserver the powerful generalization capability of the base model. The total number of trainable parameters in this lcage-scale model is approximately 4B, with all attention parameters randomly initialized.
A.2 TRAINING DETAILS
4DMoT The entire VQVAE model is trained from scratch using the AdamW optimizer with 31 0.9, B22 = 0.99, a weight decay of 1 x 10-4, and a batch size of 32 per GPU (256 in total). We train for 200K iterations with learning rate of 1 x 10-4, followed by additional 100K iterations with a reduced learning rate of 1 x 10 During training, joint coordinates are randomly scaled and shifted within range of 0-10% to augment diversity. The sampling frames of video and motion sequence are randomly chosen from {33, 49, 81, 97, 129}, with the stride randomly selected from (1, We adopt float32 precision to ensure stable codebook learning.
MV-DIT We optimize both the 6B and 18B models using the AdamW optimizer with 31 = 0.9, 82 = 0.99, a weight decay of 1 x 10-2, and batch size of 4 per GPU (32 in total). The models are trained for 30K iterations (approximately 12 H100 days) with a learning rate of 10-5 and 300 warm-up steps. During training. videos are resized and randomly cropped to the nearest resolution bucket, exposing the model to a range of resolutions and aspect ratios to improve generalization across diverse video qualities and sizes. The video sampling frames are randomly chosen from {33, 49, 81, 97, 129), with the stride randomly selected from 2]. We adopt bfloat precision for efficient training and DeepSpeed ZeRO-2 (Rasley et al. 2020) to reduce memory consumption.
A.3 EVALUATION DETAILS
We follow the evaluation settings of Realisdance-DD DiT (Zhou et al. 2025). We adopt DDIM (Song et al. 2020) for sampling, performing 50 inference steps. The motion classifier-free guidance scale is set to 3.0. The text prompt is always set to "a person is dancing" and the text classifier-fre free guidance scale is set to 6.0. Since some methods, c.g., (Tan et al. [024]] [aan et al. 2025). do not report the FID-VID (Balaji et al. 2019) metric in their original papers, we re-evaluate them under the same settings. For other reported metrics, we directly use the values from the respective papers.
B MORE DETAILS OF DATASET CURATION
Shot Segmentation We use AutoShot (huu et al., 2023), an automated shot boundary detection algorithm, to detect shot boundaries and segment raw videos into coherent, temporally continuous
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human daancing with smoothh, naturaal and expressive mation.
Image Encoder
Text Encoder
text image Context
VAE DD
cuption (optional)
padding
masked latents
Mask
4D Metion Attention
Scale Shiä
Self Attention
Layer Norn
Cross Attention
Scale & Shif
3D VAE
PFM
unconditional
motion tokens
latents
video sequence
randomly replace
DiT block
4D Motion Tokenizer
extraction
packing
Output
SMPI.joints
Figure 6: Architecture of MTVCraff-188. To demonstrate the versatility of our approach and fur- ther improve performance, we scale the model a larger DiT and enable joint text-motion control. Here, zero-padding aligns the motion token dimension with the DiT hidden dimension.
shots. This critical to eliminate abrupt scene changes and ensure that each clip maintains smooth temporal coherence, providing reliable foundation for subsequent quality filtering operations.
Pose Estimation For each segmented clip, use NLF-Pose (Sárándi & Pons-Moll 2024 to
estimate frame-wise SMPL (Loper et al., (023) joint parameters, along with the corresponding con- fidence scores for each joint. These confidence scores reflect the uncertainties of the predicted poses, providing measure of reliability for downstream processing. During pose estimation, the camera fixed with a 555 field of view along the image's longer side and centered principal point.
Single-person Sub-clip Extraction Unfortunately, the current version of MTVCraft cannot sup- port multi-person animations with different poses. Thus, focus extracting continuous sub-clips (more than 33 frames) from each video containing only a single human pose with valid predictions across all frames. In other words, frames with no pose multiple poses detected are excluded.
Pose Uncertainty Filtering We compute the average of the maximum joint uncertainty across all frames for each video and discard the top 10% most uncertain videos, as they typically contain extreme pose errors. Additionally, remaining subset and observed highly accurate motion estimations. When projected back to 2D, the 3D poses show strong visual alignment with the 2D keypoints detected by DW-Pose (Yang al., 2023]. While few poses may still exhibit minor imperfections (c.g., occasional missing frames), such diversity, as a data augmentation technique, enhances the tokenizer's robustness.
manually inspected 200 randomly sampled clips from the
Visual and Motion Quality Assessment For the remaining clips, we evaluate four complemen- tary metrics to assess overall quality: (1) Aesthetic score: we use the LAION-Aesthetics predictor
(Schuhmann et al. 2022), which a linear estimator built on top of CLIP (Radford et al. 2021),
to predict the aesthetic quality of images. (2) Optical flow magnitude: we use the UniMatch model (Xu et al., 2023) to compute the optical flow between frames, assessing the extent of motion. (3)
Laplacian blur score: apply the Laplacian operator using OpenCV [ to detect blurry frames. (4)
OCR text ratio: we use CRAFT (Baek et al.) 2019) to detect text regions and estimate the proportion of text within each frame, filtering clips dominated by textual content.
The thresholds for these metrics are set to 5.0, 2.0. 100, and 0.05, respectively. Clips that fail to meet any of the above quality thresholds discarded. Through this rigorous filtering process,
OpenCV:
http://///////////////////////// al 1apl laplace, aallaeepperator..
html
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reference image Figure 7: Failure Case. Precise hand control is challenging due to the lack of hand supervision.
target image
original pose
reconstructed pose
final image
obtain final dataset of 30K high-quality motion video clips featuring clear frames with minimal textual content, continuous and consistent single-person motions, and smooth temporal transitions. Finally, manually inspected subset of clips confirm the overall quality of the curated dataset.
C MORE DETAILS OF MODEL SCALING
As shown in Figure the scaled 18B model introduces additional text-image joint control branch, which concatenates the reference image features from the first frame with text embeddings. These fused features are then used to perform woos-attentiion with the vision latents, allowing the model to jointly reason over textual instructions and visual appearance. This mechanism enables fine-grained text control over attributes such as identity, motion, and style, enhaneing semantic-level control over generation and improving consistency with the reference frame across time.
To preserve identity from the reference image, we adopt the same strategy as Wan-2-1-12V. Specifi- cally, we pad the reference frame zo temporally with all-zero frames to match the length of the target video. The sequence is then encoded into latent representations 4 E f using a frozen
3D VAE. These latents are concatenated with a binary mask and the noisy video latents {2 to construct the composite vision latents zvision which are defined as:
Zge VAE(ZeroPad(zo))
(9)
visiion = Concat (2ref, mask, 21) E R x2exhxx
(10)
Here, the binary mask (with channels) accounts for the VAE's temporal compression rate of 4. The resulting zvision is then patchified and projected into the attention space, enabling interaction with the noisy latents and facilitating identity preservation during self-attention in the DiT blocks.
D LIMITATION AND DISCUSSION
While MTVCraft achieves impressive performance across diverse scenarios, it still presents certain limitations. First, the model may generate inaccurate results when the base model cannot handle well. Second, as shown in Figure 7 precise hand articulation remains a challenge, as clear and detailed hand motion is underrepresented in our SMPL motion-video dataset.
In addition to the technical limitation, we recognize broader concerns in the use of MTVCraft, such as potential misuse involving unauthorized identity manipulation violation of data copyrights, especially when animating reference images sourced from social platforms. MTVCraft must not be misused to fabricate harmful, misleading, disrespectful content, such mocking individuals or distorting cultural heritage. We request the responsible use of MTVCraft and plan to adopt safe- guards such consent verification and watermarking, especially in public-facing applications.
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Active
66.8%
2.5%
Frequent
Underutilized
1.00
-0.75
-0.50
-0.25
0.25
0.50
0.75
1.00
(a) Distribution of code usage rates
(b) Distribution of pairwise code similarity
Quantitative Analysis of Codebook. The left panel demonstrates that nearly 70% of the codes remain active during inference, indicating efficient utilization of the encoding space. The right figure shows that the cosine similarity of most code pairs close to 0, confirming the model's ability to learn discrete latent space characterized by highly decorrelated representations.
Figure 8:
S
Figure 9: Reconstruction Performance of 4DMoT Unseen Gymnastics Data. Each group consists of three images: the original image (first column), the extracted original pose (second col- umn), and the reconstructed pose (third column). All poses are visualized as 3D joint skeletons, projected into 2D image space using joint coordinates. Our motion VQVAE demonstrates strong generalization to unseen motion data and achieves accurate and robust reconstruction quality.
E
MORE DETAILS OF 4D MOTION TOKENIZER
E. TOKENIZING STRATEGY: DISCRETE VS. CONTINUOUS
We adopt discrete tokens instead of continuous ones for four main reasons. (1) Quantization sup- presses low-level noise and encourages the model to capture high-level semantic motion patterns. This is similar to how tokenization benefits language models. (2) A unified codebook allows motion sequences to be represented by compact set of motion primitives, which improves generaliza- tion to unseen motions while avoiding redundant variability. (3) Discrete tokens can be seamlessly integrated into DiT through embedding lookups, which makes training more efficient and stable compared to handling high-dimensional continuous trajectories. (4) Prior motion generation works like (Zhang al. 2025 Hosseyyn et al. 2025) also leverage discrete tokens, but they are defined in
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SMPL-paramette space. By instead tokenizing joint coordinates, approach provides more direct positional information aligned with pixel-level generation, thereby facilitating effective learning.
E.2 TOKENIZATION VS. RENDERING IMAGES
We adopt tokenization rather than rendering images as motion conditions for two key reasons. First, tokenization enables the model to directly learn semantic 4D motion information. For example, in 2D renderings it is difficult to distinguish whether a person appears small due to actual body size or because they are far from the camera. In contrast, tokenized joint coordinates explicitly preserve the depth dimension, providing unambiguous and compact motion cues. Second, tokenization de- couples motion from absolute position and shape variations, thereby reducing the risk of overfitting to specific human appearances. Rendered images, by contrast, inherently encode biases such as po- sition, scale, and limb length, which may lead to poor generalization across diverse characters and viewpoints. Overall, tokenization provides robust, interpretable, and geometry-awe aware representa- tion of motion, which is better suited for controllable video generation than image renderings.
E.3 ANALYSIS OF CODEBOOK
To evaluate the efficiency of our codebook utilization, we conduct statistical inference on a test set comprising randomly selected 6400 motion samples. The codes are categorized into three usage fre- quency levels based on predefined thresholds: underutilized (<1%) active (1 (1%-15%), and frequent (> 15%) shown in Figure (a). The low frequency of frequent codes (2.9%) reflects an effi- cient selection of core features for reconstruction, minimizing overfitting to local training patterns. The broad distribution of active codes (66.8%) ensures expressive diversity, allowing the model to capture wide range of patterns and preventing homogenization of the reconstruction. Mcanwhile, moderate redundancy of underutilized codes (30.3%) improves the robustness of the tokenziation process, allowing the model to support richer feature combinations. This percent (i.e., 30.0%) is much lower than the extreme unused code rates in VQ-VAE (e.g. 50% with large codebook size (Zhu al. 20224)). This balanced utilization pattern validates our effective codebook optimization, showing that it achieves both compactness and diversity while avoiding codebook collapse.
Moreover, we conduct comprehensive analysis of the codebook's latent space assess the di- versity of its entries. Specifically, we computed pairwise cosine similarities between codes and visualized their distribution as shown Figure (b). The results show that most code pairs exhibit near-zero similarity, indicating significantly uncorrelated characteristics. This finding confirms that the model successfully constructs a discrete latent space with high representational independence.
E.4 RECONSTRUCTION PERFORMANCE
Finally, to directly assess the effectiveness, we evaluate the reconstruction quality of the motion VQVAB unseen gymnastics motion sequences, which represent challenging and highly dynamic test case. As illustrated in Figure 9 our model can accurately reconstruct complex human poses, even in highly dynamic motion scenarios. All results visualized 3D joint skeletons rendered in 2D image-pixel space. The reconstructed poses closely match the original inputs, demonstrating the VQVAE's strong generalization capability and its ability to preserve spatial-temporal structure.
The results from these three experimental groups collectively demonstrate the effectiveness and suitability of the proposed 4DMoT for the downstream character image animation task.
F MORE DETAILS OF 4D MOTION ROPE
Positional encoding is critical for modeling spatial-temporal dependencies. Removing it leads to significant performance degradation, consistent with prior observations 3D RoPE for Cog VideoX and Wan. In our study, explore several positional encoding strategies and find that 4D RoPE is best suited for 4D motion tokens. Without it, the model struggles to converge. In this section, we provide detailed implementation and analysis of our proposed 4D RoPE design.
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Algorithm 4D RoPE of Motion Tokens
Require:
Dataset-wide mean joint positions mean-joints E RRxx3, number of latent frames T after 4x downsampling, and attention head dimension D.
1. Extract spatial coordinates:
x mean joints|:,
< mean joints
<- mean_joint.s
< (0.1,..,7711}
2. Centralize spatial positions:
② + mean (r)
i +-= mean y
2 mean
3. Compute ID RoPE for each axis (see Equation
(cos,, sinr) E xDD((244 + (t
(c08g, sing) (d + RoPE( D/4)
(008y, 8in,) E [2 <- RcPE(ý, D/4)
(cosg, sin,) E Rxx(/44)xx &- RoPE($, D/4)
4. Broadcast time RoPE over all joints:
(cos,, sin,) R /DD/4xx)) <- Repeat (e Sin,), dim 1, repeats
5. Broadcast RoPE over all frames:
sing R (DxA)x) <- Repeat (cos,,, ,), dim O, repeats 7)
(008y, E (DX))x(/\XXx +- Repeat ((cosy, sin,), dim = 0, repeats T)
(cos,, sing) E (Dxx4x < Repeat at (cos,,,in. ) dim = 0, repeats = T)
6. Concatenate positional encodings across channel dimensions:
freqs.com cos < Concat / (cos;, COSz, c0sy, cO82) freqs_sin < Concat (sin,, sing, sing return freqs.cos, freqs_sin
F.1
4D ROPE DESIGN AND IMPLEMENTATION
To enhance the spatial-temporal relationships of 4D motion tokens, we design the 4D RoPE. For each motion token, we compute its positional encoding based on the corresponding 4D coordinates (t, y, 2), where t denotes the frame index. The spatial coordinates (æt, y, z) are centralized by subtracting the global mean joint position, which is computed over the entire dataset by averaging all joints across all frames along the joint axis. This centralization ensures that the positional encoding remains consistent and invariant to global spatial shifts. For each of the four dimensions (1, x. y/. =), we compute sinusoidal RoPE features independently according to Equation [ with each contributing a quarter of the total attention head dimension, i.e., D/4. Temporal RoPE features are then broadcast across all joints, while spatial RoPE features broadcast across all frames. This ensures that each motion token is equipped with the corresponding and structured 4D positional encoding, enabling precise modeling of both motion dynamics and spatial structure. The detailed procedure of 4D motion RoPE is provided Algorithm [ For vision tokens, we use the image height and width as the æ and y coordinates, respectively, and z zero (representing no depth variation). This design enables natural interaction between vision and motion tokens in 4D space.
F.2 VISUALIZATION AND ANALYSIS OF 4D ROPE
To visualize the advantages of our proposed design, Figure 10 (a) presents the cross-attention maps in different attention layers. The vertical axis represents motion tokens across frames and joints, while the horizontal axis corresponds to vision tokens across frames and pixels. To better visualize the impact, resize the images to resolution of 512 x 512. When positional encoding is omitted, attention maps tend to be structureless, indicating difficulty in capturing useful relationships. In con- trast, when 4D Rotary Position Embedding (RoPE) is applied, the attention patterns become in- creasingly structured across layers, suggesting that the model benefits from explicit spatial-temporal positional cues, enabling effective interaction between vision and motion representations.
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w/ 4D RoPE
200
200
2200
-2000
-100
0
100
Block 0
Block 14
Block 28
Block 42
X.Aaut
Visualization croos--ttttctioon maps without RoPE
Visualization of used
Figure 10: Effectiveness of 4D RoPE for Motion- Vision Interaction. (a) Cross-attention maps at different Transformer blocks show that 4D RoPE enables structured interactions between motion and vision tokens. (b) Visualization mean joint coordinates across the dataset, used to compute 4D RoPE, providing typical spatial cues that facilitate consistent cross-modal modulation.
Furthermore, we visualize the mean joint positions of the dataset used in our 4D RoPE design. As shown in TO (b), the result exhibits standard human skeleton composed of 3D joint coordi- nates. These averaged joint positions serve as the spatial information for the 4D RoPE calculation, enabling the model to encode relative spatial relationships effectively and consistently across differ- ent motion sequences. This design not only enhances the robustness of cross-modal interaction but also aligns motion and vision tokens a physically plausible manner.
G MORE QUANTITATIVE EXPERIMENTS
Table presents the quantitative results Fashion (Zablotskaia et al. 2019) test Our MTVCraft consistently achieves superior scores across all metrics, demonstrating its effectiveness in modeling motion and preserving identity. These results further validate the advantages of directly leveraging 4D motion tokenization over conventional pose-rendered image based methods.
H MORE ABLATION STUDY
Ablation on Fashion Dataset Table 4 summarizes the ablation results. Consistent with the con- clusions derived from Table 2 the default design achieves the best performance. This highlights crucial role of both discrete differential motion representation and explicit positional encoding in stabilizing motion learning and improving generalization.
Ablation of Motion-aware CFG Figure II presents the qualitative and quantitative evaluations of motion-aware CFG scale. On TikTok (Jafarian & Park (021) benchmark, CFG scale of 3.0 yields the best performance, particularly for the FVD metric. For the FID-VID metric, the seale appears have minimal impact. For visual comparisons on the right, increasing the CFG scale enhances pose alignment, but it also introduces more artifacts and potentially degrades quality.
Additional Ablation For MTVCraft-I881 we further examined several alternative designs. Re- placing zero-padding with a linear or MLP layer did not converge within 10K steps, suggesting that the simple projection is insufficient to stabilize large-scale motion learning. We also attempted to inject a pretrained SMPL-paarametr space tokenizer (Guo et al.) 2024) into the DiT backbone, but training collapsed in the early stages. These observations justify the necessity of our discrete differential motion representation (i.e., joint coordinates) and explicit 4D positional encoding.
I TRAINING CURVES
we plot the training loss curves of our 4DMoT and MV-DiT. 4DMoT ex- hibits rapid convergence, with the loss quickly decreasing early in training. In contrast, MV-DIT
As shown Figure 12
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Table 3: Quantitative Results Fashion (ablotskaaa al. 2019)
Benchmark.
Model
PSNR
SSIM
LPIPS!
FID
FVD1
FID-VIDJ
MuscPose
Hu
2024
22.20
0.896
0.067
14.95
96.17
10.94
MooraAA
2024)
20.83
0.880
0.093
27.74
149.66
10.13
ControlNeXI P eng
2024
18.48
0.853
0.132
13.82
143.02
14.60
Animate-X Tan ct al.
2024)
22.15
0.893
0.069
10.11
70.47
11.87
MimicMotion (Zhang al.
2024a)
23.80
0.913
0.061
15.40
80.89
8.17
RealisDance-Dil (Zhou et al.
2025
23.33
0.908
0.053
10.81
72.94
Unianimate-DiT (Wang al.
2023
23.52
0.907
0.060
12.79
88,36
6.12
MTVCraft-6B
23.42
0.917
0.059
8.91
67.42
4.56
MTVCraft-I8B
23,90
0.923
0.057
8.74
64.88
4.41
Table 4: Ablation Study on Fahsion Zablotskaia al. 2019)
Benchmark.
Model
Choice
PSNR
SSIM1
LPIPS
FIDJ
FVD.
FID-VIDJ
w/o quantize w/o differential motion
22.95
0.897
0.066
9.65
70.23
5.09
4D MT
23.18
0.905
0.067
9.12
68.51
4.87
w/ 3D quantization
22.84
0.884
0.070
10.23
69.60
5.24
w/ dynamic
21.17
0.839
0.081
78.25
w/ learnable PE
20.84
0.821
0.096
12.38
89.22
7.45
4D MA
w/ ID temporal RoPE
20.56
0,801
0.105
14.07
98.61
8.17
w/ 2D spatial RoPE
20.38
0.813
0.099
14.25
96.38
8.55
w/ 3D spatial RoPE
20.77
0.812
0.108
13.95
99.48
8.34
w/o
19.29
0.763
0.124
113.24
10.26
Our Default Design
23.42
0.917
0.059
8.91
67.42
4.56
6.31
830
FID
FID-VID
FVD
24
10 20 23 48
50 50
10 28
30
26
Reference
Target
CPG 1.0
CPG 3.0
CPG 9.0
Figure 1I: Ablation of Motion-aware CFG. A higher CFG scale leads to better pose alignment, but also introduces more artifacts. In our experiments, scale of 3.0 achieves the best trade-off.
shows oscillatory convergence, with fluctuations in the loss curve before stabilizing. This difference highlights the distinct training dynamics of the two models, with the light motion tokenizer achiev- ing faster convergence while the relatively heavy DiT model requires more refinement for stable learning. After training for 100 epochs, both models performed exceptionally well.
EVALUATION METRICS
In this section, we provide detailed formulations of our evaluation metrics.
PSNR (Hore & Ziou| 2010):
First, we compute the mean squared error for each frame:
MSE-
1
ΣΣΣ -
(11)
HWC
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Anewap Loss Epech
SC4
04.
322
04.5
myy hwl
021
030
013
0.00
(a) Training loss curve of the 4D motion tokenizer.
(b) Training loss curve of the video DiT model.
Figure 12: Training loss curves of the 4D motion tokenizer and 4D motion-guided video DiT model. The tokenizer demonstrates smooth convergence with decreasing reconstruction and commitment loss, while the video DiT model gradually learns motion-aware video generation.
Then, we define Peak signal-to-noise ratio as:
PSNR 20 logio
255
(12)
/MSE
SSIM (Wang et al. 2004):
For each frame f and color channel c, first measure structural similarity:
SSIM; SSIM( c), fe(-.,,)).
(13)
Then, structural similarity index is the average score across all frames and channels:
T
SSIM
ΣΣ SSIM
(14)
TO
LPIPS (Zhang et al. 2018):
For each video frame, let (x(-) denote the feature map from the &-th layer of a pretrained back- bone network (e.g., AlexNet Krizhevsky et al., 2012)), and let wy denote the learned channel- wise weights. The Frame-leve perceptual distance is defined as:
4 = L
Σ E=1 H&W,
|wee o (de(T) Ge (f)))))
(15)
where H₂ and Wz denote the spatial dimensions of the L-th feature map. The video-level LPIPS score is then computed by averaging across all frames:
1 LPIPS
Σ 4
(16)
T
FID (Heusel et al. 2017):
We measure frame-lewe level visual quality using activations from a pretrained Inception- V3 network Szzgedy et al. 2016). Let (nttd Iiid) and (1 In(1) denote the Gaussian statistics of real and generated frames, respectively. The FID score is then defined as:
FTD 1/
pd TY Idd
2
(17)
FVD (Unterthiner al. 2018):
We assess temporal realism using features extracted from the Inflated 3D ConvNet (I3D) Car-
reira Zisserman 2017). Videos are split into non-overlapping 16-frame segments. Let (14,00 DNd) and (JL Eyrd be the Gaussian statistics of real and generated video segments,
FVD fed
1z
fed T( + Eidd 2(
(18)
FVD-VID (Balaji et al. 2019):
To capture long-range temporal consistency, we aggregate 13D embeddings by averaging all clip- level features within each video, yielding single descriptor per sequence. The statistics (14, E,) and (yy.En) then estimated over these video- descriptors. The distance is defined as:
FVD-VID
||,
15 THEE, E, 1/2
(19)
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K ETHICS STATEMENT
This work is conducted with the aim advancing research in controllable video generation. We acknowledge that all experiments and analyses are performed in compliance with the ICLR Code of Ethics Besides, we certify that this submission complies with the submission instructions as described on https://icle wetaacc.ccc/ooeeeeeeeeeeeeee
horuthorGuidee We claim that MTVCraft is intended solely for ethical research and creative applications, and acknowledge poten- tial risks such as misuse for identity manipulation misleading content.
L REPRODUCIBILITY STATEMENT
The overall model design described in Section S while architectural details, hyperparameters. and optimization settings are provided in Section 4 and further elaborated in Appendix A Additionally, we release all the codes and show many cases in Supplementary Material. These resources provide the necessary methodological details and ensures that readers can reliably reproduce our results.
M USE OF LARGE LANGUAGE MODELS
After completing the writing of this paper, employed large language model (LLM), e.g., ChatGPT-5 solely for language-focure focused grammar and style proofreading. Specifically, we pro- vided the PDF version to the LLM identify and correct potential grammatical typographical errors. All technical content, ideas, and experimental results were produced by the authors.
N MORE QUALITATIVE COMPARISONS
In this section, we provide additional qualitative comparisons to further demonstrate the effective- and robustness of MTVCraft across wide range of scenarios, character appearances, and motion types. As shown in Figure T3 and 15 our MT'CCrff consistently demonstrates the best performance with high-quality character motion and high-fidelity appearance across different styles.
O MORE VISUALIZATION RESULTS
In this section, provide additional visualizations. Figure [4 demonstrates powerful zero- shot generalization to unseen diverse characters or even objects. Figure [16 provides more cases showing that
able transfer complex 4D motion sequences unseen subjects. Figure I7 shows additional open-world character animations conditioned on different motion sequences, where MTVCraft consistently achieves high identity consis- tency and motion accuracy across vari- ous styles. Figure 18 shows human character animations conditioned on different motion sequences, where MTVCraft perfectly preserves both identity and motion accuracy across diverse real human characters. We also provide many videos generated by our ATVVCraf in Supplementary Material.
"ICLR Code of Ethies: Btttis//hhttsss//...c..cooo////e
http:: httpt:://cattttt...o
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Animate Anyone
Uni-
Mimic Motion
Control- NeXt
Stable Animator
Condition
Ours
Animate
Figure 13: More Comparisons (1). Our MTVCraft consistently demonstrates the best performance with high-quality human motion and high-fidelity appearance across different styles and scenes.
56
56
56
T Craft
Figure 14: More Visualization Results (1), These cases demonstrate MTVCraft's powerful zero- shot generalization to unseen diverse characters or even animals, objects.
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Animate Anyone
Uni- Animate
Mimic Motion
Control- NeXt
Stable Animator
Condition
Ours
Figure 15: More Comparisons (2). Our MTVCraft consistently demonstrates the best performance with high-quality human motion and high-fidelity appearance across different styles and scenes.
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10
mone
10
114
Figure 16: More Visualization Results (2). Each row shows an animation conditioned on differ-
ent motion sequence. These visualizations showcase our strong zero-shot generalization capability to complex motions, diverse unseen subjects, including animals and even objects.
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Published as a conference paper at ICLR 2026
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Figure 17: More Visualization Results (3). Each row shows open-world character animation conditioned on different motion sequence. These visualizations showease open-world animation results featuring virtual human characters. MTVCraft consistently achieves high identity consis- tency and motion accuracy across various styles.
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Published as a conference paper at ICLR 2026
Figure 18: More Visualization Results (4). Each row shows human character animation con- ditioned different motion sequence. Our MTVCraft consistently preserves both identity and motion accuracy across a wide variety of scenarios and diverse real human characters.
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Reviews:
[{"review_id": "PuxKROYgLT", "reviewer": "Reviewer_KU6U", "summary": "This paper proposes MTVCraft, which shifts pose guidance from 2D renderings to direct tokenization of raw 4D motion (SMPL joint coordinates) via a VQ-VAE motion tokenizer (4DMoT). These motion tokens condition a Motion-aware Video DiT (MV-DiT) equipped with 4D motion attention and 4D rotary positional encoding, aligning spatio-temporal structure during generation. The system scales from a 6B (CogVideoX-5B) to an 18B backbone (Wan-2.1-14B) and supports multi-control (text + motion) with simple integration, delivering state-of-the-art results on TikTok and Fashion benchmarks. Design ablations and negative results further motivate coordinate-space tokenization over parameter-space alternatives.", "questions": "Please see the weaknesses.\n\nTypos: \"Fahsion\" should be \"Fashion\" in Table.4's header.\n\nOverall, this paper offers a clean, well-justified shift from 2D renderings to discrete 4D motion tokens, demonstrates superior results on two standard benchmarks (with scaling to 18B), and provides enough architectural detail to be useful for practitioners. The main remaining gaps ( camera parameterization, brief data quality discussion) are addressable in the camera-ready and do not undermine the core contribution. I believe this paper will contribute to the community, so I vote clear acceptance.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. Clear paradigm shift from 2D renderings to discrete 4D motion tokens, with a well-articulated rationale (coordinates vs. parameters) and an architecture that uses motion tokens natively (4D RoPE + motion attention).\n\n2. Strong empirical gains on both TikTok and Fashion. The 18B model improves FID/FVD while modestly raising SSIM/PSNR over strong baselines. \n\n3. Scalable & practical. The 18B integration is straightforward (zero-padding alignment), and the paper documents unsuccessful alternatives (linear/MLP projection, SMPL-parameter tokenizer), which is valuable for reproducibility and future work. \n\n4. This paper shows cross-identity animation results across different species, e.g., human pose to bird, fish or even chair.", "weaknesses": "1. Camera view handling are implicit. There’s no explicit camera-parameter conditioning. The method relies on data diversity and 4D tokens. This is workable, but leaves questions about view transitions or long-term 3D consistency.\n\n2. As SMPL joint are hard to accurately estimated, how the authors ensure that annotation quality? Besides, why don't use SMPL-X which includes hands?\n\n3. For the heavy 18B model, will the inference cost of the proposed model be 10x or even 100x of the previous U-net based models, so that the comparison is not fair enough? It will be better to report the inference cost in performance comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iOR8QWkiIV", "reviewer": "Reviewer_M8dQ", "summary": "This paper proposes MTVCraft (Motion Tokenization Video Crafter), a novel framework for character image animation that directly models raw 3D motion sequences (referred to as 4D motion) rather than relying on traditional 2D-rendered pose images. This is achieved by introducing a 4D Motion Tokenizer (4DMoT) to quantize 3D motion into compact tokens, which are then used to condition a Motion-aware Video DiT (MV-DiT). The approach effectively decouples motion from pixel-level alignment and appearance biases, showing strong zero-shot generalization to arbitrary characters, diverse styles, and non-human subjects. MTVCraft achieves state-of-the-art performance on benchmarks like TikTok and Fashion. The shift from 2D pose to 4D motion tokens is a significant and positive step forward for controllable video generation.", "questions": "Why 3D/4D information is more useful than 2D? Can the authors provide some evidence?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.\tThe paper addresses a limitation of current methods by replacing fragile 2D pose images with robust, compact 4D motion tokens derived directly from SMPL joint coordinates.\n2.\tExperimental results indicate the effectiveness of the proposed method.", "weaknesses": "1.\tThe method introduces a new, independently trained component: the 4D Motion Tokenizer (4DMoT). Training this additional encoder (a VQVAE) adds complexity to the overall pipeline and represents an extra component that must be learned, stored, and maintained, potentially limiting the ability to scale the entire framework compared to methods that use off-the-shelf 2D pose estimators. During inference, the system requires an additional forward pass through the 4DMoT encoder to generate the motion tokens from the raw SMPL joint coordinates. While the tokens are compact, the initial encoding step introduces additional inference latency and computational cost that is not present in 2D-based methods (which often use pre-calculated 2D maps). The paper should provide a detailed breakdown of the latency and resource consumption of the 4DMoT during inference compared to the latency of the main MV-DiT model to justify this extra computational step.\n2.\tThe entire pipeline is contingent upon the accuracy of the upstream SMPL joint sequence estimation (using NLF-Pose in this work ). Errors or noise in the initial 3D pose data will directly impact the quality of the 4D motion tokens and thus the final animation quality.\n3.\tThe teaser figure shows multi-character animation results. But the motions of different characters are same. How can we animate only one character in the multi-character image?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "42gHeOXrYl", "reviewer": "Reviewer_1LKS", "summary": "This paper introduces a new framework that directly models raw 3D motion sequences to animate arbitrary characters from reference images. Unlike previous methods that rely on 2D pose renderings, MTVCraft encodes 3D joint trajectories into compact 4D motion tokens using a 4D Motion Tokenizer (4DMoT), which preserves spatial-temporal dynamics and eliminates the need for pixel-level pose alignment. These tokens are then integrated into a Motion-aware Video Diffusion Transformer (MV-DiT) featuring 4D motion attention and 4D positional encodings, enabling expressive, disentangled control of motion and appearance. Implemented on both CogVideoX-5B and Wan-2.1-14B backbones, MTVCraft achieves state-of-the-art results, with superior zero-shot generalization to unseen characters, styles, and non-human subjects.", "questions": "The codebook size of 8,192 with a 3072-dimensional embedding is relatively large compared to those commonly used in motion generation models. Would decreasing its dimension or size help reduce unused codes or improve resolution?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "A key strength of integrating a motion-generation pipeline into video generation lies in its ability to provide explicit temporal and structural control over motion, resulting in more coherent and realistic dynamics than end-to-end pixel-based video synthesis. By introducing intermediate motion representations, such as in motion generation domain [1,2], the framework captures fine-grained spatial-temporal cues that general video models often overlook. This separation of motion from appearance allows the generator to synthesize consistent, expressive, and physically plausible movements while preserving identity and style, effectively bridging human motion understanding with visual generation. Moreover, as demonstrated in previous literature [3], coupling motion modeling within video diffusion enables strong generalization to arbitrary characters. Also due to the modular paradigm, the method scales naturally across different backbones, whether smaller transformer-based models like CogVideoX or Wan-2.1, since the motion tokens are from unified SMPL.\n\n[1] Momask : MoMask: Generative Masked Modeling of 3D Human Motions, cvpr 2024\n[2] SALAD : salad skeleton-aware latent diffusion for text-driven motion generation and editing, cvpr 2025\n[3] AnyMoLe : AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models, cvpr 2025", "weaknesses": "A potential weakness of this paradigm is that the 4D motion compression via 4DMoT is conceptually straightforward and not architecturally novel. The encoder-decoder with vector quantization closely follows standard VQVAE formulations, and while it effectively transforms SMPL joint trajectories into compact motion tokens, it does not introduce fundamentally new techniques in motion encoding or representation learning. However, despite this structural simplicity, the usage and integration of such motion tokenization within a large-scale video generation framework remains a meaningful contribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)