Towards Multiple Character Image Animation Through Enhancing Implicit Decoupling
Authors: Jingyun Xue
Hongfa Wang
Qi Tian
Yue Ma
Andong Wang
Zhiyuan Zhao
Shaobo Min
Wenzhe Zhao
Kaihao Zhang
Heung-Yeung Shum
Wei Liu
Mengyang LIU
Wenhan Luo
Venue: ICLR 2025 Poster (2025)
Keywords: character image animation
video generation
diffusion model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Controllable character image animation has a wide range of applications. Although existing studies have consistently improved performance, challenges persist in the field of character image animation, particularly concerning stability in complex backgrounds and tasks involving multiple characters. To address these challenges, we propose a novel multi-condition guided framework for character image animation, employing several well-designed input modules to enhance the implicit decoupling capability of the model. First, the optical flow guider calculates the background optical flow map as guidance information, which enables the model to implicitly learn to decouple the background motion into background constants and background momentum during training, and generate a stable background by setting zero background momentum during inference. Second, the depth order guider calculates the order map of the characters, which transforms the depth information into the positional information of multiple characters. This facilitates the implicit learning of decoupling different characters, especially in accurately separating the occluded body parts of multiple characters. Third, the reference pose map is input to enhance the ability to decouple character texture and pose information in the reference image. Furthermore, to fill the gap of fair evaluation of multi-character image animation, we propose a new benchmark comprising about 4,000 frames. Extensive qualitative and quantitative evaluations demonstrate that our method excels in generating high-quality character animations, especially in scenarios of complex backgrounds and multiple characters.

Published as a conference paper at ICLR 2025
TOWARDS MULTIPLE CHARACTER IMAGE ANIMATION THROUGH ENHANCING IMPLICIT DECOUPLING
Jingyum Xue Hongfa Wang Qi Tian?: Yue Ma² Andong Wang", Zhiyuan Zhao", Shaobo Min', Wenzhe Zhao?, Kaihao Zhang'. Heung- Yeung Shum Wei Liu", Mengyang Liu Wenhan Luo
'Shenzhen Campus of Sun Yat-sen University, Tencent Hunyuan, TTinghua Univerisity *HKUST, "Harbin Institute of Technology, Shenzhen https: : //wwt//a//////////////mmm////aat -tilltiiiii aii-nntie annimtt ion. github. /
Figure 1: Pose-controllable single-character image animation (top) and dual-character image ani- mation (bottom) given the reference image.
ABSTRACT
Controllable character image animation has a wide range of applications. Al- though existing studies have consistently improved performance, challenges per- sist in the field of character image animation, particularly concerning stability in complex backgrounds and tasks involving multiple characters. To address these challenges, propose novel multi-condition guided framework for character image animation, employing several well-designed input modules to enhance the implicit decoupling capability of the model. First, the optical flow guider calcu- lates the background optical flow map as guidance information, which enables the model to implicitly learn decouple the background motion into background constants and background momentum during training, and generate a stable back- ground by setting zero background momentum during inference. Second, the depth order guider calculates the order map of the characters, which transforms the depth information into the positional information of multiple characters. This facilitates the implicit learning of decoupling different characters, especially in ac- curately separating the occluded body parts of multiple characters. Third, the ref- erence pose map imput enhance the ability to decouple character texture and pose information in the reference image. Furthermore, to fill the gap of fair eval- uation of multi-character image animation, propose new benchmark prising about 4, 000 frames. Extensive qualitative and quantitative evaluations demonstrate that our method excels in generating high-quality character anima- tions, especially in scenarios of complex backgrounds and multiple characters.
INTRODUCTION
Character image animation task targets animating given static character image to a video clip using a sequence of motion signals, such as pose, while preserving the visual appearance. It has attracted much attention and has been extensively explored in research (Zhang et al. (022). Ex- isting character image animation can be divided into three categories: baN-based, 3D-based, and
* 'These authors contributed equally to this research
I Project leader
1 Corresponning author

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Published as a conference paper at ICLR 2025
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Figure 2: Challenges faced by existing methods: (a) By overlaying video frames, it is observed that models generate unreasonable backgrounds. (b) Models struggle to accurately identify characters in multi-character image animations, resulting in incorrect character identities. (c) Models have difficulty in accurately generating the occluded body parts of multiple characters.
diffusion-based frameworks. GAN-based methods (Siarohin al. 2019 Wang et al. 2021 lever- age warping function to transfer the reference image into the target pose, and then the GAN model employed to generate the missing parts. 3D-based methods (Jiang et al. 2023) reconstruct char- acter avatar from monocular videos and then render the avatar into character video based on the pose sequence. Diffusion- based methods generate refined character animation by adding conditional control. For example, Hu et al. (2023) generate a series of coherent action videos by incorporating pose sequences, while Xu et al. (2023) do so by integrating semantic map sequences.
Despite generating visually plausible videos, existing methods still face several challenges. GAN- based methods often struggle to generate realistic animations due to their limited ability to transfer motion, which affects details such as hair movement, and facial expressions. 3D-based methods (Jiang et al., 2023 Yu et al. 2023) are generally limited by the precision of SMPL and struggle to derive hand and facial details. In addition, 3D-based methods cannot fill in the empty back- ground left after character movement. Besides, 3D-based methods perform poorly in generating high-quality texture details and clothing movement. In contrast, diffusion- based methods alleviate these problems due to their powerful generative capability. However, diffusion-based methods still face serious challenges when generating higher-quality and more complex character animations. As shown in Fig. [] existing methods often have the drawback of generating unreasonable backgrounds. Specifically. due to the camera shake and/or video effects popularly present the background, the model is affected by the noise, leading to abrupt changes, flickering, and artifacts in the background. Additionally, when generating animated videos with multiple characters, existing methods tend to generate chaotic character identities and erroneous occluded body parts.
Our work is dedicated addressing these challenges. Through our investigation, we have the fol- lowing findings. First, the background instability results from the model's low robustness to noisy data with background variations in the training set. The common solution to construct a high- quality training set without background variation, which is costly and limits the size of the dataset, especially considering that data for multiple characters is scarce. We aim for the model to achieve strong generalization with sufficient and cheap training data with background variations. A fea- sible solution is to decouple the noise features from the video, improving the utility of the noisy data. Second, the errors multiple character image animation primarily stem from that the model cannot further decouple multiple character features into individual character features. Perhaps we can enable the model to learn this implicit decoupling process through extensive multiple-character data, but more economical and expeditious approach is to provide guiding information to direct the model in learning this process. These findings indicate that it extremely important introduce the implicit decoupling ability into the model for the task of multiple-character image animation.
In this work, to address the aforementioned challenges, we propose a diffusion- based framework for multiple-characte image animation, allowing for the input of various types and amounts of guid- ance information. Based on the mentioned insights, we carefully design three guiders to enhance the implicit decoupling capability of the model. (1) We design an optical flow guider that calculates the background motion optical flow map as guidance. This enables the model to implicitly learn to de- couple the background motion into background constants and background momentum during train- ing, and generate a stable background by inputting zero background momentum during inference. (2) We present the depth order guider calculating the order map of the characters, which transforms
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Figure 3: The overview the proposed framework. The left half illustrates the data flow of the multiple condition guiders, with green and black arrows denoting training data flow, and red and black arrows indicating inference data flow. The gray box represents different inputs for training and inference. The right half shows the denoising U-Net and ReferenceNet.
the depth information into the positional information of multiple characters in terms of their relative front and back positions. This facilitates the implicit learning of decoupling different characters, especially in accurately separating the occluded body parts of multiple characters, leading to more stable multiple-character animation. (3) We introduce the reference pose map to enhance the ability to decouple character texture and pose information in the reference image. Additionally, we pro- pose a multi-character benchmark including about 4, 000 frames, which empowers the community to comprehensively evaluate the generation ability in complex multiple character image animations. To the best of our knowledge, we are the first to collect such a benchmark. We conduct extensive quantitative and qualitative experiments to illustrate the superiority of our approach.
In summary, our contributions are as follows:
We propose multi-condiiion guided framework with multiple guiders for multiple- character image animation, which exhibits strong robustness against noisy data with un- stable backgrounds. By leveraging large-scale raw data for training, this framework effec- tively addresses the multiple-character image animation.
We enhance the implicit decoupling capability of the model. Technically, the optical flow guider decouples the background momentum to ensure background stability, the depth order guider provides multiple character positional information to address the occlusion among body parts, and reference pose guider inputs the source pose align the character with the target pose.
We address the lack of a benchmark multiple character image animation. new bench- mark called
Bench is introduced, which contains about 4, 000 frames for fair and comprehensive evaluation.
Extensive quantitative and qualitative evaluations are conducted using two public datasets and Multi- Caati-Chhrrater Bench. The results show the superiority of the proposed method.
2 RELATED WORK
Diffusion Models for Video Generation. Diffusion models excel in generating high-quality con-
Ma et al., 2023 2024c| Chen et 2024b Feng et al.) 2024; Tan et al., Ho et al. (2020): Song et al. (2020) propose to generate images using Diffusion models. Many studies (Wu al. (002) adapt image synthesis pipelines for video gener- ation. Khachatryan et al. (2023) incorporate motion dynamics in generated frames via cross-frame attention in zero-shot style. [uuo et al. (203b)) finetune a soug-and--laa motion module that can be integrated into any text-to-image models to obtain animations in personalized style. Zhhan jet al. (2024); Chen et al. (2024a) achieve both text-to-video and image-to-videe to-iieoo generation high resolution. Besides, Guo et al. 2023a) extract semantics from images condition T2V generation.
tents (Esser et al.
2023
2024 Kong et al.
2025).
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Figure 4: The pipeline for calculating the skeletal dilation map and optical flow map. The optical flow map is obtained by superimposing the skeletal dilation map onto the optical flow vector map.
Cose-Controllable Character Image Animation. Generating realistic character video from the driving signal (e.g,, key points, semantic maps) has been extensively studied recent years. Early approaches (Mirza & Osindero 2014] Wang et al. 2021 employ GAN-based models for conditional video synthesis. Wang et al. (2019) use conditional GANs with optical flow, temporal consistency, and multiple discriminators for diverse pose videos. Chan et al. (2019) transfer movements from a source person to a target person through keypoints. [Siarohin et al. (2021) incorporate consistent regions that describe locations, shapes, and poses. To address the pose gap between objects in the source and driving images, [Zhao & Zhang (2022) effectively align object poses using thin-plate spline motion estimation and multi-resolution occlusion mask. With the development of the diffu- sion model, existing methods use pose sequences to guide the character video generation (Zhang al.) 2023). Karras et al. (2023) propose finetuning framework adapt the Stable Diffusion model to pose-and-immae image guided video synthesis model. [Hu et al. (2023) design ReferenceNet to merge detailed human body features, ensuring character appearance consistency, while employ- ing pose guider and temporal modeling for controllable and continuous movements. To enhance the performance, WWag et al. (2023) disentangle the control conditions (i.e., character foreground, background, and poses) by introducing multiple ControlNets for different feature embeddings and a human attribute pre-training framework is proposed. Moreover, Ma et al. (2024a) propose a two-stage training scheme to address the lack of comprehensive paired video-poss datasets.
3 PRELIMINARIES
Video Latent Diffusion Models. The powerful text-to-image (T2I) model with the addition of temporal motion modules endows it with the ability generate video (Guo et al. (023b), i.e. video latent diffusion model (VLDM), Specifically, inserting temporal motion module after spatial attention enables the T2I model to generate video. The temporal motion modules run across tempo- ral frames to enhance motion smoothness and content consistency. The encoder of VAE (Kingma 2019) compresses the video clip V1.n to latent space feature E(Uion) ). Besides, the initial input tensor 2 E Rexxx of the T21 model should add temporal dimension and repeat it n times, becoming 2im E | VLDM uses U-Net RRonebergger al. 2015) or DiT (Peebles & Xie 2023) to estimate the noise, with the loss function of
Welling 2013 Razavi al.
L == (rL)))) (V((,))) [[le (zi f, c)ll
where e.(-) is the network for predicting noise. c denotes the conditional information. t represents the timestep of denoising process and zf.,, is the intermediate result of denoising at timestep t.
METHODOLOGGY
Given a reference image To and pose sequences (Po. P2..,, we aim to generate character video clip with both plausible motion and faithful visual appearance (foreground and background) regarding the reference image. To address the issues of identity emorr and body occlusion in multi- character image animation, or background instability, building large-scale high-quality datasets and conducting extensive training is costly and laborious. In contrast, enhancing the implicit decoupling ability of the model through input guidance more economical and feasible. To achieve this, we propose novel framework equipped with multi-condition guiders, shown in Fig.
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Figure 5; The pipeline for calculating the depth order map, which strictly separates characters and assigns distinct values. The yellow module "Skeletal Dilation" is shown in Fig. 4 (a).
4.1 OPTICAL FLOW GUIDER
As shown in Fig. (a), when directly training on noisy data, the model unable achieve back- ground alignment due to the interference caused by noise. As result, there are abrupt changes, flickering, and artifacts in the generated background. We propose using background optical flow maps guidance to direct the model in learning the implicit decoupling process, which decouples the background into constant and a momentum. Here, background momentum represents the rela- tive motion of the current frame's background compared to the previous frame, which is equivalent to the optical flow. After decoupling the background motion, the model can learn to generate stable backgrounds even trained noisy data. During inference, we input zero background motion to achieve stable background. Unlike the work (Liang et al. 2024) using optical flow to enhance tem- poral consistency, use the motion vector information optical flow to decouple noise features.
Fig. presents the pipeline for calculating the background optical flow map in the training stage Yang et al. 2023; Güler et al. 2018 [Snn et al., 2018). The optical flow estimator Efow (OpenMMLab 2222)) predicts optical flow maps from adjacent frames (V1, v2... UN ). The dilation operation is then applied on pose skeletons (p1, Po..., PN] ) to obtain binary mask sequences .N),, which are used for defining the control region. We blend optical flows with the binary mask and encode them using 8 layers of inflated 3D convolution. The region in blended optical flows is set to 1.0. In inference, we directly set the background optical flow, i.e., the background momentum, to zero. Formally, the optical flow guider implemented as:
M; = =D (p:),
&Yow = Sop ((1 - MMs) ① Esow (U; U4-1))
(3)
where D represents the dilation operation, @ is Hadamard product, and gop is the optical flow guider.
4.2 DEPTH ORDER GUIDER
Concurrent methods struggle to accurately identify different characters and generate correct identi- ties. They lack spatial order information about different characters in front the camera, which is critical for generating occluded body parts. To address this issue, we design a depth order guider that can simultancously enhance the implicit decoupling of characters into individual ones and provide characters' spatial order information. Specifically, it calculates the depth order map of characters, decoupling multiple characters into individuals while providing their sequential positional/depth relationships in front of the camera. The regions of different characters are separated by assigning distinct values, with occluded body parts being strictly assigned to characters positioned in the front.
Fig. 5 Illutrrttes calculating the depth order map during training. For each frame, we extract individ- ual character poses and calculate their skeletal dilation maps. Subsequently, we merge the character- wise skeleton dilation maps to get the intersection and subtract to derive the non-intersection area. Next, split by characters,
shown by the green arrow in Fig. we have the character wise non- intersection area maps. Then, we align them with the depth maps, compute the average depth of the non-intersection region for each character, and sort them by the average depth. We assign values to each character's ' region based on their position ranking. For example, as shown in Fig. [5] the front- most character region gets "yellow value" while the second front character region gets "red value".
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Table 1: Quantitative comparison on Tiktok dataset. The best and second-best results are indicated in red and blue respectively. AnimateAnyone' is trained on our noisy dataset. Ours is trained on the Tiktok training set.
Method
FID
SSIM
PSNRT
LPIPSC
LI1
FID-VID
FVD1
MRAA (Siarohin et al. 2021
54.47
0.672
29.39
0.296
3.21E-04 -4
66,36
284.82
TPSMM (Zhao & Zhang
2022
53.78
0,673
29.18
0.299
3.23E-04
72.55
306.17
DreamPose Karras
2023
79.46
0.509
28.04
0.450
6.91E-04
80.51
551.56
DisCo (Wang
51.29
0.699
28.70
0.333
1.10F-04
61,41
379.56
DisCo+ [Wag et al. 2023)
48.29
0.713
28.78
0.320
1.03F-04
52.56
334.67
MagicAnimate (Xu ct al.
202.3
32.09
0.714
29.16
0.239
3.13E-04
21.75
179.07
MagicPose (Chang el al.
2023
25.50
0.752
29.53
0.292
0.81E-04
46.30
216.01
AnimateAnyonn [Hu
2023
0.718
29.56
0.285
171.90
AnimateAnyonee
54.42
0.685
29.01
0.316
1.06--04
47.93
236.28
Ours* Ours
29.15
0.735
29.61
0.287
0.79E-04
35.28
153.47
27.70
0.760
29.70
0.272
0.73E-04
14.30
117.81
Table 2: Quantitative comparison on TED-talks dataset, The best and the second-best results are indicated in red and blue respectively. AnymateAnyonef is trained on our noisy dataset.
Method
FID
SSIMf
PSNRT
LPIPS
L1L
FID-VID
FVD.
MRAA (Siarohin et al. 2021
50.36
0.762
31.90
0.266
0,50E-04
82.79
493.02
TPSMM (Zhao & Zhang
2022
23.71
0.771
32.30
0.252
0.49E-04
32.12
260.67
DisCo Wang et al. 2023
75.48
0.575
27.99
0.309
1,21F-04
66.18
393.04
DisCo+ Wang
202
63.28
0.596
28.12
0.300
1.11E-04
55.81
343.20
Aaimate (Xu et al. 2023
41.58
0.529
28.28
0.310
1.73E-04
33.61
223.54
MagicPose (Chang el al. 2023
23.39
0.723
30.08
0.236
0.81E-04
27.53
214.23
Moore-Auee AnimateAnyone
25.93
0.710
30.99
0.310
0,46E-04
41.20
262.49
AnimateAnyonef
47.68
0.691
29.59
0.283
1.15E-04
30.07
241.76
Ours
18.21
0.779
30.88
0.198
0.46E-04
10.24
81.73
The result is the depth order map. During inference, the calculation of the average depth value the skeleton dilation areas is skipped. Instead, directly compute the order values of the characters in the reference image and assign these values to the corresponding areas of the character dilation skeletons. Given a training video v, where the i-th frame denoted as U; supposing that there are characters with pose skeleton {PI,..,psH in U;. The training processes can be illustrated as
Ui,,, = (pi....
(4)
mi, = di,j 11 n (asx) jE {1..J},
(5)
kE(l....,
fsort (m4,1 2 (.,,),., Cdepth.jj = mi.j ④ Traak, + mi,j ④ Cdepth.j+1) Ct1 = O,
rank,
mii O (V))).
(6)
(7)
Gdepth gáp (1
(8)
where D represents the dilation operation, (z,j denotes the character-wise skeleton dilation map. intersection operation and m., is the non-intersection region of each character. fa represents the depth calculation network, fsort is average depth sorting (descending order), and rank, is the depth ranking character j. Traak, denotes the value assigned to j based on depth ranking and Cdepth.j is the depth order map of the farthest characters. Idp represents the depth order guider.
4.3 REFERENCE POSE GUIDER
The positions of characters in inference images and pose sequences often exhibit inconsistencies. Diffusion- based methods employ spatial cross-attention facilitate the interaction between the pose frame and reference image, enabling it to learn character texture information from the reference im- ages and accurately map them to the corresponding pose positions. We realize that this process involves the model implicitly decoupling the character's pose and fine-grained texture information from the reference image, and then mapping the texture to the target position by aligning the char- acter's pose with the target pose. Consequently, we can assist the model in this decoupling process by introducing the reference pose as a prior. Since the reference pose map and the pose frame are consistent in format, the model can directly align the pose and map the characters efficiently without

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Published as a conference paper at ICLR 2025
Reference
MRAA
TPSMM
DisCo
DisCo+
MagicAnimate MagicDance
Ours
GroundTruth
Figure 6: Qualitative comparisons between baselines and our approach on dataset Tiktok (Top three rows) and TED-talks (bottom two rows). MRAA and TPSMM* present these methods utilizing ground-trume -tunh videos as driving signals.
calculating poses from the reference image. In this way, the model can reduce the learning load and focus on mapping character textures. Given the reference image æ, the Cref-pose can be written as
Cref 9rp (fs (z))
(9)
where fa represents the skeleton extraction network and Irp denotes the reference pose guider.
4.4 SKELETAL DILATION MAP
Skeletal dilation map is used to mask character and separate it from the background, and Fig. illustrates its calculation. The skeletal dilation map does not perfectly cover the character area compared to segmentation maps, but it achieves better results. Using segmentation map as mask strictly requires highly accurate body segmentation maps driving signals. However, given only an inference image and pose sequence during inference, it challenging to generate a segmentation map sequence that perfectly aligns with the characters in the reference image. The discrepancies in segmentation map accuracy between training and inference lead to model misalignment and poor performance, In contrast, skeletal dilation maps can be generated directly from pose sequences and generalize well to characters of varying heights and body types. For both training and inference, we use the same skeletal dilation maps, enabling the model learn to adapt its mask range and become less sensitive to mask precision. Further details can be found in the appendix (Sec. B.3)
4.5 MODEL ARCHITECTURE
Fig. shows our framework. Specifically, the reference image is compressed using VAE and then fed into ReferenceNet (Cao et al. 2023). It interacts with the features in the U-net model through spatial cross-attention. Meanwhile, the CLIP-encoded reference image is used as the prompt to replace the text prompt in the diffusion model. The three proposed conditions and the pose drive signal sequence are encoded by the same structured guider, which consists convolutional layers. These encoded features are then incorporated into the initial noise latent. The whole training objective can be formulated as
Cuultii Cposs Cfow Gdepth Cref
(10)
C (g(wggg()).c.. - [[]e 6n <a(2) (mults)
(11)
where Cmulti denotes the overall control condition. The definitions of Uias Cizns and of are analo- gous Eq.

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Published as a conference paper at ICLR 2025
Table 3: Quantitative comparison on Multi- Chlti--haracttr Bench. The best and the second-best results
are indicated in red and blue respectively. AnimateAnyone is trained on our noisy dataset.
Method
FIDI
SSIM
PSNRT
I.IPST
1.11
HID--VID
FVDJ
DisCo Waan et al. 2023
77.61
0.793
29.65
0.239
7.64E-05
104.57
1367.47
DisCo+ Wang et al, 2023
73.21
0.799
29.66
0.234
7.33E-05
92.26
1303.08
MagicAnime Xu 2023
40.02
0.819
29.01
0.183
6,28E-05
19.42
223.82
MagicPose (Chang
2023)
31.06
0.806
31.81
0.217
4.41E-05
30.95
312.65
Moore- Anyoeee
33.04
0.795
31.44
0.213
5.02E-05
22.98
272.98
AnimateAnyone
35.59
0.796
31.10
0.208
4.87E-05
22.74
236,48
Ours
26.95
0.830
.6.6
0.173
4.0TB-05
14.56
142.76
Reference
DisCo+
MagicAnimate
MagicDance
Moore-Animate
Ours
GroundTruth
R
Figure 7: Qualitative comparisons the
bench.
5
EXPERIMENT
Dataset. The robustness of our model to noisy data enables direct training on unfiltered videos. We collect 4000 character-action videos of 2M frames our training set. We analyze the level of contamination in the noisy dataset in the appendix (Sec. A.
Training strategy. Our model trained two stages. In the first stage, we freeze the VAE (Van Den Oord et al. 2017) and CLIP (Radford et al. 2021) image encoder, and remove the temporal mo- tion modules. U-Net, ReferenceNet, and multiple-condition guiders are trained to align their spatial generative capacities. In addition, we utilize the weights of Stable Diffusion v1.5 (Rombach et al., 2022] to initialize this training stage. In the second stage, we incorporate temporal motion modules along with all parameters from stage one, aiming to endow the model with temporal smoothness. Besides, we use the weights of AnimateDiff v2 [Guo al. 2023b) to initialize this training stage.
Implementation Details. We sample 16 frames of video, resize and center-crop them to a resolution of 896 x 640. Experiments are conducted on 8 NVIDIA A800 GPUs. Both stages are optimized using Adam with a learning rate 1 10 In the first stage, train our model for 60K steps with batch size 4, and in the second stage, we train for 60K steps with a batch size of 1. At inference, apply DDIM (Song al. 2020) sampler for 50 denoising steps, with classifier-free guidance (Ho) & Salimans) 2022) scale of 1.5. See the appendix (Sec. C.I for more details.
5.1 COMPARISONS
Dataset and metries. Following the previous methods (Wang et al., 2023), we evaluate our method on TikTok videos (Wang et al. 2023) and TED-talks (Siarohin et al. (021). A Additionally, due to the lack of benchmarks for multiple-character video generation, we collect 20 multiple-character dancing videos with 3917 frames, named Multi-Character. This dataset serves as benchmark for evaluating models' capabilities in generating controontrllla videos with multiple characters. Our employ conventional
evaluation metrics adhere to the existing research literature. Specifically, image metrics to assess the quality of individual frames, including LI error, PSNR (Hore & Ziou 2010), SSIM (Wang et al. 2004), I.PIP (Zhang al. 2018), and FID (Heusel et al. 2017). For
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Table 4: Quantitative ablation results on TikTok Dataset.
Method
FIDL
SSIMT
PSNRT
LPIPS..
LLL
FID-VID
FVDL
w/o. All Conditions
54.42
0.685
29.01
0.316
1.0E-044
47.93
236.28
w/o. Optical Flow
52.94
0.710
28.21
0.318
0.96F-DD4
34.35
178.48
w/o. Ref. Pose
34.74
0.740
29.13
0.285
0.82F-04
19.58
139.32
w/o. Depth Order
27.43
0.754
29.98
0.270
0.79F-04
14.50
119.26
Ours
27.70
0.760
29.70
0.272
0.73E-04
14.30
117.81
w/o. Optical Flow Condition
w/. Optical Flow Condition
Figure 8: Qualitative comparison results of ablation variants without optical flow condition. Trans- parent and overlay the images to clearly see the changes in the background.
video evaluation metrics FID-VID (Balaji et al. 2019) and FVD (Unterthiner et al. 2018). 8). we form sample by concatenating each consecutive 16 frames.
Counterparts. We compare with several state-of-the-art methods for character image animation.
(1) MRAA (Siarohin et al., (02T)) and TPSMM (Zhao & Zhang 2022) are GAN-based methods.
(2) DreamPose (Karras et al. (023), Disco (Wang et al., 2023).
MagicAnimate (Xu et al.) 2023),
MagicPose (Chang al.] 20033) and AnimateAnyone (Hu al. 2023) are VLDM-based methods.
Note that we evaluate the AnymoteAnyone reproduced by Moore Threads (Moore Threads (024) on
TED-talks and Multi-Character. (3) We reproduce Animate Anyone trained the noisy dataset.
Unified evaluation standards. We notice that not all approaches adhere to a uniform generation size. As methods with different generation sizes yield different metric results, potentially leading to unfair comparisons, we standardize the generation sizes by center-cropping and resizing to 512 x 512. Under this unified standard, we reevaluate methods that do not conform this generation size and directly refer to the original results of methods that comply.
Evaluation on Tok dataset. Table presents the quantitative comparison of the TikTok dataset. Our model performs best on five metrics and second best on the FID and LPIPS metrics. Notably, our model excels particularly video metrics FID-VID and FVD. Compared with the second-best method in FID-VID and FVD, our model shows significant improvement of 39% and 35% re- spectively. The AnimatcAnyone we reproduce is trained on the noisy dataset and performs poorly, showing that noisy data greatly weakens the model. In contrast, the module design enables model to exhibit strong robustness to noisy data and perform excellently when trained on noisy data. Additionally, Ours* trained on Tiktok training set shows slight improvement over baselines, indicat- ing our method excels when trained on clean data. The visual comparison is shown in the top three of Fig. (6] Our approach performs better in pose following and visual quality. Specifically, the first and third rows show that our method not only accurately follows the pose but also generates hand details. The second row suggests the powerful pose-following capability of our method, which is the sole approach capable of accurately generating the pose with the arm raised in reverse.
Evaluation on TED-talks dataset. As reported in Table our model achieves SOTA performance across all metrics except PSNR. Compared with MagicPose achieving the second- best of FID-VID and FVD, our model demonstrates substantial enhancement of 49% and 48% respectively. Again, it is observed that the base model performs poorly for the influence of noisy data. The bottom two rows in Fig. (6) illustrate the visual comparison. In the fourth row, only our method successfully maintains the consistency of the character holding white notes in the inference image. In the last row, our method exhibits the best performance in the pose following, with the fewest artifacts.
bench. Our results in Bibleiiiiniffiaatt outperform others across all metrics on Multi-Character bench. In terms of the video metrics FID-VII VID and FVD, we outper- form the second-best best method by 25% and 36%, respectively. The qualitative comparisons in Fig. highlight the significant advantages of our approach in maintaining identity consistency (top row), pose following (second & third rows), and preserving spatial relationships (bottom row).
Evaluation on

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Depth Order Condition
w/. Depth Condition
GroundTruth
Figure 9: Qualitative comparison results of ablation variants without depth order condition.
Reference image
reference pose
Reference image
pose
Figure 10: Qualitative comparison results of ablation variants without reference pose condition.
5.2 ABLATION STUDY
To investigate the roles of the proposed conditions, we examine three variants without Optical Flow, Depth Order, and Reference Pose, respectively. In Table the proposed full method ("Ours") with three conditions outperforms other variants, demonstrating the effectiveness of the three conditions.
Optical flow. In Table the variant without optical flow exhibits the most significant decline, suggesting that optical flow has the most pronounced positive impact on the model. The comparison results shown in Fig. X illustrate where its primary effects are background stability. We overlay transparent six frames to form the right image, enabling clear depiction of the moving region. As indicated by the red boxes, the background in the results the variant without optical flow shows noticeable shaking, whereas the background in the results of the full method remains consistently stable. This validates that noisy data with unstable backgrounds leads to the generation unstable backgrounds, whereas the incorporation of the optical flow condition
solve this problem.
Depth order. Fig. 9 validates the positive impact of the depth order condition. The red boxes highlight the overlapping areas of multiple characters. The variant without depth order condition fails to generate the hand of the character on the left placed behind the right character, instead generating malformed hand. Conversely, the full method with the depth order condition generates overlapping areas of multiple characters. i.e., the hand of the left character is positioned behind the right one, and the hand of the right character in front of the head of the left one.
Reference pose. Fig. 10 illustrates the comparison when the character position of the inference image and pose sequence are not aligned. The full method with reference pose performs better in terms of image quality and character consistency. The variant without reference pose maintains the background consistency, while failing to maintain character consistency (see the changes of clothing of the character). In contrast, the full method with reference pose effectively achieves alignment between character and pose sequence, thereby maintaining character and background consistency.
CONCLUSION
In this paper, we design three guiders to enhance the implicit decoupling ability a pose- controllable character image animation framework that integrates multiple conditions, addressing the unstable background and poor handling of body occlusions in multiple character scenes. The op- tical flow guider decouples the background to facilitate the learning of stable background generation. The depth order guider decouples multiple character features into individuals to solve the problem of multiple character generation. The reference pose guider enhances the learning of characters' ap- pearance. Moreover, we have curated and released a benchmark dataset of pose-controllable videos with multiple characters. Experiment studies show the effectiveness of our method.
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ACKNOWLEDGMENT
This work was supported in part by the National Natural Science Foundation of China (Grant No. 62372480), in part by the Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012339) in part by Huawei Gift Fund (No. HUAWEI251S02), and in part by HKUST- MetaX Joint Lab Fund (No. METAX24EG01-D).
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APPENDIX
In this supplementary material, we present:
Dataset Supplement (Sec.
Analysis of noisy training dataset. (Sec. A.I
Details of the training dataset. (Sec. A.2
Details of the Multi-character benchmark. (Sec. A.3
Methodology Supplement (Sec. B
Pseudo code of depth order guider. (Sec.
B.1
Long video inference. (Sec. B.2
Further details of skeletal dilation map. (Sec. B.3
Experimental Supplement (Sec.
More implementations. (Sec.
Unified standard for comparative experiments. (Sec. C.2
-Liiitation (Sec.
More Qualitative Results (Sec.
Additional Comparison. (Sec.
Additional Ablation. (Sec.
Additional Visualizations, (Sec. E.3
A DATASET SUPPLEMENT
A.1 ANALYSIS OF NOISY TRAINING DATASET
Distribution of Videos with Different bof-mean
Background Optical Flow Mean (boF-mean)
Density
Magnitude background optical flow mean bof-mean=1.0
bof-mean=1.5
bof-mean=2.0
bof-meanz2.0
Figure Distribution histogram and image examples of background optical flow mean.
We analyze the level of contamination in the noise dataset. Specifically, we calculate the background optical flow using skeletal dilation mask exclude character regions, then average across frames to derive the background optical flow mean for each video. Fig. I (right) shows videos with differ- background optical flow means. is observed that only when the background optical flow mean is at least less than 1, the motion of the background of the video is imperceptible to human eyes. Fig. II (left) illustrates the distribution of the background optical flow mean the training set. indicates that only about 12% of the training set videos have a background optical flow mean of less than 1. noise data proportion 88% indicates severe contamination of the dataset. Therefore, necessary to incorporate background optical flow maps into the network for training.
We randomly sample 400 videos (approximately 10% of the dataset) from dataset and analyze the distribution of videos based on the count of characters they contain. Fig. [2] (left) illustrates the proportion of videos regarding different numbers of characters in the sampling set. Specifically,
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The distribution of character counts in videos
The distribution body occlusion rates
L.75%
1.00%
50%
30
0311120 1880
75
118,
265,
66.25%
1.0
34
Single
Double
Triple
Quadrupla
80
00
Figure 12: Sampling distribution of character counts in videos and body occlusion rates in double- character videos.
single-character videos account for 66.25%, double-character videos account for 29.5%, and videos of triple characters and above account for total of 4.25%. can be concluded that the majority of videos in our dataset single-character or dual-character, with very few videos containing three or more characters.
Subsequently, we compute the body occlusion rates among characters across all sampled multi- character videos. Specifically, we calculate the intersection area of the skeletal dilation maps for multiple characters in each frame of the video, and then divide it by the union area of these skeletal dilation maps for the same frame. This yields the body occlusion rate for that frame. Subsequently, compute the average body occlusion rate across all frames of video and analyze the distribution across all videos. Fig. 12 (right) illustrates the distribution body occlusion rates in multi-character videos. We can find that 25% of the videos have a body occlusion rate within 0.05, 25% fall between 0.05 and 0.13, 25% are between 0.13 and 0.21, and the remaining 25% have body occlusion rates exceeding 0.21. It indicates that, average, the body occlusion rates of most multiple-character videos fall between and 0.21, with videos exhibiting high occlusion rates above 0.21 being rela- tively rare. Meanwhile, we directly observe that there are many multi-character videos with body occlusion rates around 0.
A.2 DETAILS THE TRAINING DATASET
Table The detailed composition of the training dataset.
Source
Videos
Frames
Proportion
Tiktok
2,493
1,379,449
68.5%
YouTube
938
435,293
21.6%
Kuaishou
424
115.101
5.7%
Bilibili
162
83,785
4.2%
We collect 4, 017 character videos with the amount of 2, 013, 628 frames as our training set. The data come from public videos on TikTok, YouTube, and other websites. The detailed composition of the training dataset is shown in Table
A.3 DETAILS THE MULTI-CHARACTER BENCHMARK
We collect 20 multiple-charee character dancing videos of 3917 frames in total, from social media, named Multi- Cilfi-Chrratter. Table 6 shows the detailed sources of Mulfi-Chon Mulfi-Character.
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Table
The source of Multi-character benchmark.
Video Name
Url
Timestamp
Daovm348PQQ
00:10-00:14
Daovm348PQQ
00:16-00:25
Daovm348PQQ
2
D
01:11-01:15
01:16-01:21 ota.aa/a.........a.....a.a.naaaa
Daovm348PQQ.4
Daovm348PQQ
5
01:22-01:25
Daovm348PQQ
02:11-02:15
Daovm348PQQ 8
00:20-00:33
00:38-00:43
00:44-00:52
jx VseYOi5A
00:32-00:37
ix VseYOi5A
00:40-00:53
YOii5A
01:02-01:07
Yx_
VseY015A
01:08-01:12
Ka3BIUsvRqE.O
00:21-00:27
ka3B/UsvRqE.
00:28-00:33
2
ht http:///.ww...uuuucc//////aaaaat
02:05
yclnNCBSrbA 0
https:/ htt.::://ww...outtube.. 1tt.1://......i......//.../.....
00:10-00:15
B METHODOLOGY SUPPLEMENT
B. PSEUDOCODE OF DEPTH ORDER GUIDER
Algorithm 1: Pseudocode for depth order map mask extraction
Input:
Given training video length N, where the i-th frame is denoted e;, and suppose that there are J/ characters on The skeleton extraction network is denoted fs. The expansion network is denoted as fa- The depth extraction network is denoted as fa. The average depth sorting (ascending order) operator is denoted as Ssort- The value assigned to rú based on depth ranking is denoted as Try. The depth
guider is denoted Idp-
Initialize Cdepth,1,0 ................................ ...... C,e = 0.
2 Initialize an array CR.
for i i= to N do
Qi.1...., Dz,J = fe (fa (v;))
for to J do
mijj Di.j (1 (1- UjE(t,....))) (aia))
Fi....,Ty= Soort (m²,1 @ Sa (ui),... mijJ 4 Sa (u;))
for r. = do
Cdepth,i.ry Mi,ry 2 Lrg + ((1 Mi.r;) ) Grepth...(y-11))
CR [i] Cdepthii.r
Cdepth 9dp (CR) return Cdepth
B.2 LONG VIDEO INFERENCE
We employ the overlap method for long video inference maintain the consistency of long video. As illustrated in Fig. [3] we divide the pose sequence into multiple shorter segments for inference, with overlapping parts between adjacent segments. For the overlapping parts of segments,
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Time Sequence:
Time Sequence
Overiop
XN
XN
XN
Ref Image
Pred Video
Figure 13: The pipeline for inferring long videos.
perform addition and averaging to generate temporal smoothing between the two segments. In this work, we perform inference on every 16 frames with stride of frames, and then stitch them together using an overlap of 8 frames.
B.3 FURTHER DETAILS OF SKELETAL DILATION MAP
Skeletal Dilation
MX
FOG M
M
Pose Extraction
characters
Sequence
Skelefal Dilaion
Dilate-SkeleTo
Figure 14: The pipeline for calculating skeletal dilation map.
We use skeletal dilation map as mask cover the character region in the "optical flow maps" and "depth order maps". The purpose of masking off the character regions in "Optical Flow Maps" to prevent character motion from affecting the decoupling of the background. Additionally, the skeletal dilation map is used to refer to different character areas in "Depth Order Maps", facilitating the decoupling of characters into individual ones.
Fig. 14 shows the pipeline for calculating the skeletal dilation map, which directly generated from the pose frame. Maybe concern is that skeletal dilation map often does not the character region comprehensively. However, in our attempts, the skeleton dilation map achieves better results compared the segmentation map that fully covers the human body. We analyze the reasons as follows: (1) The consistency between training and inference processes enables the model to learn to ignore the deviations of skeleton dilation maps. Specifically, skeletal dilation map represents only rough but not precise character regions, during both training and inference. When using the skeletal dilation map as mask during training, the model learns to map rough character regions to precise character regions and applies this capability during inference. Similarly, research on the "regional image animation" task and "modify region animation" task (Ma et al. (024b) have confirmed that models animate the objects represented by the mask region, rather than animating the mask region itself. Fig. 1.5 (a) shows that when the skeleton dilation map does not perfectly cover the character, the character still separates perfectly from the background, and the character animation remains out- standing. Fig. 15 (b) illustrates that the parts of the character that exceed the coverage range of the skeletal dilation map can also be animated perfectly. In contrast, Fig. [5 (c) shows bad case where the portion of the clothing extending beyond the coverage of the skeletal expansion map too large, resulting poor animation effects for the clothing. (2) The use of the skeleton dilation map exhibits high tolerance and insensitivity to small amounts of noise. Specifically. the skeleton dilation map requires the model to learn to adapt mask range, resulting less sensitivity to mask precision. This enables the same skeletal dilation map to perform well across various scenarios involving dif- ferent body types and clothing, demonstrating strong generalization capability. In contrast, the use of segmented images imposes strict requirements for high accuracy, and segmentation maps with different body types or clothing, or even slightly different fingers can lead to significant degrada- tion. (3) The discrepancies in segmentation map accuracy between training and inference lead to model misalignment and poor performance. During training, using the segmentation map generated from the original video will make the model highly sensitive errors. However, given only the
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(a)
(b)
(c)
Figure 15: The results using skeletal dilation map mask.
inference image and pose sequence during inference, it is challenging to generate segmentation map sequence that perfectly aligns with the characters in the reference image. Therefore, minor misalignments in the inference segmentation map can lead to significant degradation of the results.
C EXPERIMENTAL SUPPLEMENT
C. MORE IMPLEMENTATIONS
In the data processing, we utilize the DWPose (Ynng et al. 2023) to extract pose sequence from videos, and PWC-Net (Sun et al. 2018) from the open-source toolbox MMFlow (OpenMMiLab)
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Table Inconsistent inference standards across all methods.
Method
Inference Size
Center-Crop Uninterrupted Frames
MRAA Siarohin et al.
2021
x84
TPSMM Zhao Zhang
2022)
384x384
DreamPose (Karras et al.
2023
512x640
DisCo (Wang al. 2023)
256x256
MagicAnmatt (Xu et al.
2023
512x512
MagicPose (Chang et al.
2023)
512x512
Table 8: The ablation experiment the inference standards of Disco+.
Inconsistent Standards
FID_
SSIM
PSNR
LPIPS.
FID-VID
FVD.
origin size, w/o center-crop
30.75
0.668
29.03
0.292
59.90
292.80
256x256, w/o uninterrupted frames
28.31
0.674
29.15
0.285
55.17
267.75
512x512, w/o uninterrupted frames
48.29
0.713
28.78
0.320
52.56
334.67
512x512, w/ uninterrupted frames
48.29
0.713
28.78
0.320
47.73
312.49
2021 to calculate optical flow vectors. Additionally, we use the Depth Anything (Yang et al. 2024)
extract depth maps from videos.
When conducting long video inference, we perform inference every frames with stride of frames, and then stitch them together using an overlap of 8 frames. Besides, we resize and center- crop the "Reference Image" and "Pose Sequence" to uniform resolution of 896 640 pixels (512 x 512 pixels in comparative experiments). We apply the DDIM sampler for 50 denoising steps, with classifier- free guidance.
Regarding the inference resources, our method requires 24GB of VRAM to generate a 640 896 video, 16GB of VRAM for 480P (480 854) video, and 12GB of VRAM for 360P (360 640) video. Thus, our model can run on most commodity-level GPUs with the necessary adjustment to an appropriate video generation resolution.
C.2 UNIFIED STANDARD FOR COMPARATIVE EXPERIMENTS
We notice in the comparative experiment that not all approaches adhere to uniform inference size and other inference details. As depicted in Table 7 there are primarily three inconsistent standards that may affect fair comparisons. In the following, the method Discot will be used example to illustrate how inconsistent standards affect the fair comparison of metrics, as shown Table First, Table 7 shows some works resize without center-crop, which can result in significant differences in the test set, as illustrated in Table g Second, as shown in the comparison between the second and third of Table g different inference sizes result in different metric values. Table 7 shows the inconsistent inference sizes of each method. Third, compared to other methods, Disco has a bug in measurement, resulting in fewer video segments being sampled when calculating FID-VID and FVD. This will lead to a decrease in FID-VID and FVD of Disco, as shown in Table
Because methods with different standards result in different metrics, potentially leading to unfair comparisons, we standardize the inference sizes by center-cropping and resizing to 512 x 512, and fix the bug in the measurement of disco. Under this unified standard, reevaluate methods that do not conform to this inference size and directly refer to the data from the original literature of methods that do comply. Most previous works directly refer to the inconsistent statistical data from other works for comparison, resulting in unfair comparisons. We are the first to conduct comparative work under unified standard.
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D LIMITATION
In this work, we dedicated to addressing the challenges in pose-controllable multiple-character animation. However, there are still several problems we have not resolved. First, similar to most diffusion-based approaches, our model struggles generate highly refined facial and hand details. Second, our model also struggles to generate substantial swaying of long skirts, Hanfu, or other large-area clothing very well, as shown Fig. 15 (c). Third, our model also faces challenges in handling complex multiple-character scenarios, such as those involving four or more characters, or extensive swapping of positions among characters.
E
MORE QUALITATIVE EXPERIMENTS
E.1
ADDITIONAL COMPARTSON
Reference Image
AnimateAnyonet
Ours
GroundTruth
Figure 16: Qualitative comparison between ours and Anyoate.nyoneff.
We reproduce Anyonte on our noisy dataset, but achieve worse results compared to original AnimateAnyonn as shown in Tables [ 2 and
3 This indicates that our noisy dataset's poor
video quality significantly weakens the model. Fig. I6 illustrattes the qualitative comparison between ours and AnimateAnyone*.. In the top three rows, it can be observed that the backgrounds generated by AnimatcAnyonet exhibit numerous artifacts. arm when generating occluded body parts of multiple characters. In contrast, ours addresses these issues, demonstrating that the proposed conditions have a significant positive impact on the model.
the bottom two rows, AnimatcAnyonef loses
Fig. 17 llsttraess a comparison of facial regions between ours and baselines on the Tik dataset. can be observed that our method generates the optimal character face, particularly achieving consis- tency with the reference image in terms of expressions and hairstyles. Note that we use the reference image, rather than the groundtruth, as the correct sample.
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MRAA
TPSMM*
DisCo
DisCo
MagicAnimate MagicDance
Ours
Reference
GroundTruth
A
Figure 17: Qualitative comparison of facial regions.
E.2 ADDITIONAL ABLATION
FRUP
Reference Image
w/o. Depth Order Condition
w/. Depth Order Condition
GroundTruth
Figure 18: Additional visualizations of ablation variants without depth order condition.
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Reference image
w/o. reference pose
Reference image
pose
Figure 19: Additional visualizations of ablation variants without reference pose condition.
E.3 ADDITIONAL VISCALIZATIONS
Reference
Animation
A
Figure 20: Additional visualizations, example 1, different poses for different characters.
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Reference
Animation
Figure 21: Additional visualizations, example 2, character rotation.
Reference
Animation
Figure 22: Additional visualizations, example 3, three or four characters animations.
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Figure 23: Additional visualizations, example 4, single character dance Just Because You'l 're So Beautiful.
Figure 24: Additional visualizations, example single character dance the viral kemusan.
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Figure 25: Additional visualizations, example 6, single character dance the rabbit dance.
0o
24
30
00
Figure 26: Additional visualizations. example 7, multiple character dance the viral kemusan.
M
M
Figure 27: Additional visualizations, example 8, multiple character dance the rabbit dance.
26


Reviews:
[{"review_id": "USFk2a5Ppy", "reviewer": "Reviewer_sEw7", "summary": "In this work, the authors propose solutions for challenges in image animation, such as instability under complex backgrounds and multi-person scenarios. To enhance quality in these situations, three additional guides are introduced: optical flow, depth order, and reference pose map. The network design is similar to previous methods which include ReferenceNet, denoising U-Net, and inserting a temporal module in the second stage. Experiments demonstrate that the proposed methods outperform existing approaches, but more experimental results should be provided.", "questions": "1. I am curious about scenarios where two individuals have significant interactions.\n\n2. I think that the results in Figure 2 should reference the specific previous methods used for these issues.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This paper proposes to integrate three additional guiders, optical flow, depth order, and reference pose map to improve the animation performance under complex backgrounds and multi-character situations (mostly two persons). \n\n2. The animation results are impressive and better than other methods, which demonstrate the efficiency of the proposed solutions.", "weaknesses": "1. The claim that the depth order map provides positional information for multiple characters is somewhat confusing. In practice, you first use the dilate-skeleton map to determine the positions of individuals, and subsequently obtain the depth order using depth information. \n\n2. The results in the paper only involve two individuals. To support the claim of handling multiple characters, it would be beneficial to include examples with more than two characters.\n\n3. The pose sequences for two persons in a video are all the same, I think more results in different pose sequences for different characters should be provided.\n\n4. The network design follows the previous methods, like MagicAnimate, which includes ReferenceNet, denoising U-Net, and inserting a temporal module in the second stage. I think Figure 3 can be improved to better demonstrate the network architecture.", "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": "PCcoApa7F9", "reviewer": "Reviewer_7Np5", "summary": "The paper proposes a new image-based pose retargeting/character animation diffusion model. The key contributions lie in how the proposed method handles (1) noisy backgrounds, which may degrade the quality due to spurious correlations, (2) explicitly capture the depth-order to enable more accurate generation for multi-character retargeting, and (3) enhance decoupling of appearances and body pose representation to achieve better texture quality.\n\nThe experimental results show that the proposed method achieves higher perceptual quality, both quantitatively and qualitatively.", "questions": "I am overall positive about the submission. It would be nice if the authors can discuss a bit more about the following questions, in addition to the weaknesses raised above:\n- What is the inference cost? Would it be able to run on commodity-level GPUs (e.g., Nvidia 2060, 3070, etc). I may have missed it, but it seems like only A100 is mentioned, and that is for training.\n- How does the ```Ref Pose Guider``` handle multi-character skeleton maps. Do we assume there is a fixed number of character in the video?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The core strength of the method lies in the attention to the details. Specifically, \n- By explicitly considering the depth order, the method disambiguates the spatial arrangement in multi-character settings, and therefore enables the model to learn better, and render with higher fidelity.\n- By providing the pose for the reference image, the model can better decouple the texture and pose information (although it is still implicit). Despite being simple, this design appears to have a great positive impact on the generation quality (in Figure 10).\n- The background optical flow mitigates the spurious correlations that hinder the model from focusing on character animation.\n\nOverall, the presented method is sound, and the effectiveness of each proposed component is sufficiently verified through their experiments.", "weaknesses": "- The proposed method is computationally heavy (line 417-420, needs 8 A100 GPUs for training), making it less accessible to possible end users. This is, however, a common issue among diffusion-based methods, not particular to the paper.\n- The depth-ambiguity is not completely resolved. One can see in the supplementary video 00:44, the top-left character has their hand rendered to the back incorrectly.\n- The method focuses on front-facing poses. It is unclear how it performs on extreme poses with 360-degree rotation, and also poses like handstand, etc.", "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": "q35nxMQZZi", "reviewer": "Reviewer_TMXE", "summary": "Existing human animation methods struggle with multi-character animation tasks and are difficult to train on data with unstable, noisy backgrounds. This paper addresses these challenges with two main motivations: to decouple background motion from static elements and to separate multiple characters. To this end, the authors propose a novel framework that includes an optical flow guider and a depth-order guider.\n\nThe optical flow guider encodes background motion, while the depth-order guider captures the spatial relationships among multiple characters. Additionally, a reference pose guider is introduced to encode the pose of a reference image, simplifying the textual remapping process.\n\nEvaluations on multi character benchmark demonstrate that this method generates high-quality character animations when trained on noisy web-collected datasets.", "questions": "- Can the authors provide animation results for more challenging scenarios, such as dense crowd animations or high occlusion ratios?\n    \n- In Table 1, the model trained on a noisy dataset outperforms the AnimateAnyone baseline trained on a clean TikTok dataset. Is this improvement due to the dataset or the proposed modules? If the improvement stems from the dataset, could the authors provide a more detailed analysis of dataset quality (e.g., pose diversity, data scale, character domain diversity)? If the improvement is due to the proposed modules, the authors should train their model solely on the TikTok dataset to directly compare with the baseline methods.\n    \n- In the inference stage, can the optical flow guider take a specific optical flow map as input to achieve results with camera rotation effects?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper identifies a key challenge in training human animation models on in-the-wild data—the instability of background motion—and proposes a pipeline to address this issue.\n    \n- An optical flow-based conditioning module is introduced, enabling human animation methods to train on data with background instability, which helps scale the approach to noisy, real-world datasets.\n    \n- The paper presents a heuristic depth-order map calculation pipeline, providing simple yet effective spatial information for multi-character generation tasks.", "weaknesses": "- Although the authors claim the method can handle multiple characters, most of the results feature only one or two characters.\n    \n- While the paper includes some analysis of background motion in the training dataset, it lacks in-depth analysis related to multiple characters, such as the effect of character count or occlusion ratios.", "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": "Jl7jUQtNzL", "reviewer": "Reviewer_eUry", "summary": "The paper tackles the problem of multiple-character image animation and addresses the failures in current approaches by introducing additional control signals: 1) To alleviate the influence of non-static background, it adds the optical flows as conditions; 2) To avoid identity mixup in multi-character animation, it adds depth order guidance that labels the identity id within the skeletal dilation map. It achieves comparable performance on single-character animation benchmarks and better quality on multi-character animation.", "questions": "In addition to the weakness, can the authors clarify how the depth order map of the target pose sequences is acquired during inference? More specifically, how the depth order is determined given only the skeletons of multi-character poses?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The proposed methods are well-motivated. The authors demonstrate the effectiveness of the proposed control signals by comprehensive ablation studies.\n* The skeletal dilation map is novel to me as an alternative to the segmentation mask. The authors explain the choice and its advantage over the segmentation mask, which is sound.\n* It shows better quality on multi-character animation compared to baselines.", "weaknesses": "* The head poses are limited in all the qualitative results presented in the paper, including the figures and the supplementary videos. For example, in Fig. 1, the head poses of all characters are the same as the reference images while other parts of the body are retargeted into new poses. It makes it difficult to assess the animation quality without qualitative results showcasing more diverse head poses. This is my main concern since faces are probably the most difficult and important part of the animation. Comparing facial regions with the baselines will help the judgment.\n\n* As mentioned in the limitations, the use of the skeletal dilation map restricts the animation of clothes that cover a significantly larger area than the skeletal dilation map. Is the kernel size of dilation a crucial factor for the method? How does it impact the animation of clothes?", "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)