Character Mixing for Video Generation

## Abstract

Abstract Imagine Mr. Bean stepping into Tom and Jerry —can we generate videos where characters interact naturally across different worlds? We study inter-character interaction in text-to-video generation, where the key challenge is to preserve each character’s identity and behaviors while enabling coherent cross-context interaction. This is difficult because characters may never have coexisted and because mixing styles often causes style delusion , where realistic characters appear cartoonish or vice versa. We introduce a framework that tackles these issues with Cross-Character Embedding (CCE), which learns identity and behavioral logic across multimodal sources, and Cross-Character Augmentation (CCA), which enriches training with synthetic co-existence and mixed-style data. Together, these techniques allow natural interactions between previously uncoexistent characters without losing stylistic fidelity. Experiments on a curated benchmark of cartoons and live-action series with 10 characters show clear improvements in identity preservation, interaction quality, and robustness to style delusion, enabling new forms of generative storytelling.
Additional results and videos are available on our project page: https://tingtingliao.github.io/mimix .

## 1 Introduction

In an era where films and iconic characters are just a click away, a natural question arises: what if we could unite these beloved characters together, merging their roles and interactions into a single story?

Since the release of Sora (OpenAI, 2024) by OpenAI, fundamental text-to-video (T2V) generation models (Yang et al., 2024; OpenAI, 2024; Team Wan, 2025; Team, 2024b; DeepMind, 2024; Kong et al., 2024; kli, 2024; Team Wan, 2025) have achieved substantial progress in general video synthesis. For producing videos centered on specific or customized characters, a common approach is to leverage a reference image as input. Such personalized video generation methods (He et al., 2024; Liang et al., 2025; Jiang et al., 2024; Wei et al., 2024; Fei et al., 2025; Chen et al., 2025c) allow users to create customized content with their own images.
However, reference images alone provide visual similarity but fail to capture motion characteristics such as the character’s distinctive behaviors and interactions with the environment, and others.
Thus, while preserving the identity, these approaches often fail to faithfully capture the character’s complex behavior.
To this end, we want to develop a method that could allow the model to learn from all the footages and scripts of the characters of interest that can be collected, so as to learn not only their appearance but also motion idiosyncrasies and personality, and to generate vivid videos that maximally match their behavior traits and motion patterns.

To faithfully generate videos for a single character, one can fine-tune a text-to-video model on footage of that character. However, moving beyond single-character settings brings two major challenges.

The first is the non-coexistence challenge: characters from different shows never co-occur in any training video, leaving no paired data to model their joint interactions. To address this, we explicitly encode each character’s identity and behavior into text by annotating their names and actions in the captions. This disentangles character-specific behavior embeddings from the underlying training videos, enabling us to fine-tune one foundation model on each character’s individual footages while still allowing them to co-exist and interact naturally at inference time.

The second is the style delusion challenge:
characters often originate from domains with drastically different visual styles, such as live-action sitcoms and cartoons, which never naturally co-exist in the same videos.
Directly training on mixed styled data leads to unstable character styles, as shown in Figure [2](https://arxiv.org/html/2510.05093v1#S1.F2), where the model tries to render all characters in a uniform style matching the training data.

Figure: Figure 2: Style delusion examples. When mixing different style characters, their styles may shift undesirably. For instance, Mr. Bean looks cartoonish (top row), while Ice Bear appears realistic (bottom row).
Refer to caption: figure/images/style_delusion.png

We tackle this by introducing a style-aware data augmentation strategy that composites characters from different domains into the same video while preserving their native appearances. We find that even a small proportion of such augmented data substantially improves style preservation in cross-domain character mixing. For background ambiguities, we introduce an extra prompt for background style.

To validate our approach, we curate an 81-hour (52,000 clips) dataset featuring two cartoons (Tom and Jerry, We Bare Bears) and two realistic shows (Young Sheldon, Mr. Bean). Each clip is annotated with explicit character names and style information, supporting fine-grained control during training and inference. We further establish the first benchmark for multi-character video generation, evaluating identity preservation, motion fidelity, interaction realism, and style consistency.

Our contributions are summarized as follows:

- •
We proposed the first video generation framework for multi-character mixing that addresses both the non-coexistence challenge and the style dilution challenge.
- •
We curated an 81-hour (52,000 clips) dataset with character- and style-annotated captions, enabling controllable multi-character video synthesis across domains.
- •
We conducted extensive experiments and introduced a benchmark, showing that our method significantly improves identity preservation, motion consistency, and interaction quality compared to prior art.

## 2 Related Works

Video Generation The advent of diffusion models has transformed video generation, advancing from early text-to-video systems such as ImagenVideo (Ho et al., 2022), Make-A-Video (Singer et al., 2022), and VideoLDM (Blattmann et al., 2023), to large-scale architectures like Sora (OpenAI, 2024b), Goku (Chen et al., 2025b), Wan2.1 (Team Wan, 2025) and HunyuanVideo (Kong et al., 2024), which achieve state-of-the-art general video synthesis. However, these models remain limited in generating content with specific identities or custom subjects, motivating the emerging direction of personalized video generation, where reference visual signals guide the synthesis of videos with consistent appearance and motion dynamics.

Single-Concept Personalization Early personalized video generation methods, such as DreamVideo (Wei et al., 2024), Magic-Me (Ma et al., 2024), and PersonalVideo (Li et al., 2024), customized videos with per-subject tuning, achieving identity preservation but requiring costly optimization. More recent zero-shot approaches like ID-Animator (He et al., 2024) leverage facial adapters to enable identity-consistent generation from a single reference image without fine-tuning. Despite these advances, existing methods largely emphasize visual similarity while overlooking motion-related aspects such as unique behaviors and environment interactions.

Multi-Concept Customization Compared to sigle-concept personalization, multi-concept customization is more challenging due to identity blending, where multiple characters risk being fused into a composite scene. Video Alchemist (Chen et al., 2025c) addresses this through cross-attention–based fusion of text and image representations for open-set subject and background control, while Movie Weaver (Liang et al., 2025) employs tuning-free anchored prompts to preserve distinct identities. Other approaches, such as CustomVideo (Wang et al., 2024) and Custom Diffusion (Kumari et al., 2023), explore parameter-efficient fine-tuning and joint optimization for multi-subject composition. However, existing image-guided methods cannot leverage video and textual data during training, limiting their ability to model realistic interactions and dynamics across characters.

## 3 Methods

The goal of our method is to learn the essence of characters from large collections of video data and enable them to interact seamlessly in new, mixed contexts. Given the abundance of video series—spanning cartoons and live-action shows, our approach seeks to (1) capture characters’ unique identity and behavioral traits, and (2) enable flexible mixing of characters across styles and universes.

Our curated dataset (Section [3.3](https://arxiv.org/html/2510.05093v1#S3.SS3)) consists of TV shows and animations. We leverage not only video clips but also audio and scripts, which provide crucial cues about each character’s personality and behavioral logic. Each domain features one or multiple central characters, sometimes appearing in ensembles (e.g., Tom and Jerry), other times in isolation (e.g., Mr. Bean).

In this section, we develop a novel training scheme that introduces Cross-Character Embedding (CCE) and Cross-Character Augmentation (CCA) to achieve robust identity modeling, behavior preservation, and style-controllable mixing.

### 3.1 Cross-Character Embedding (CCE)

Faithfully reproducing a character requires learning from dynamic data that captures not only appearance but also behavior, idiosyncratic motion patterns, and contextual habits. However, images are insufficient because they omit the motion and interaction cues that define a character’s identity.

We therefore design a framework to learn the character concept embeddings across different domains. We need to tackle character disentanglement in multi-character shows (e.g., Tom and Jerry, We Bare Bears), where multiple identities must be separated within the same clip. We also face the challenge of non-coexistence of characters from different universes, who never appear together in the training data but must interact coherently at inference.

Figure: Figure 3: Model finetuning architecture (left) and data augmentation pipeline (right).
Refer to caption: x1.png

Character–Action Prompting.
Our key insight is to design a character–action captioning format that explicitly grounds each character’s identity while separating it from scene context. Unlike standard captions that describe only visual events, our captions follow the format:

[Character: <name>], <action>.
[Character: <name>], <action>.

This design ensures that embeddings encode characters as independent entities with disentangled actions and identities. During inference, the same prompting scheme enables coherent composition of characters who never coexisted in training—for example, Mr. Bean interacting with Tom and Jerry.
Although Mr. Bean and Jerry never meet each other in the dataset, the model has observed how each interacts with others in their respective domains, allowing it to generalize to cross-universe interactions. A more detailed illustration is provided in Figure [3](https://arxiv.org/html/2510.05093v1#S3.F3).

Prompt Generation.
We employ GPT-4o (OpenAI, 2024a) to automatically generate captions. For each short clip, we provide: 1) 10 sampled frames as visual context, 2) dialogue transcripts from the audio, and 3) source metadata (cartoon vs. TV series).

Scripts and plot summaries are also supplied to resolve ambiguity when a short clip lacks context. This setup enables GPT-4o to reliably identify character names and actions while minimizing hallucinations. The resulting process yields  52,000 video–caption pairs. Each character mention is annotated with [character:name] tags, which act as identity anchors and support controllable character-level generation. More details are provided in the supplemental.

Model Adaptation.
We fine-tune Wan2.1-T2V-14B (Team Wan, 2025) with Low-Rank Adaptation (LoRA) (Hu et al., 2022). Our approach is model-agnostic and applicable to any text-to-video backbone. The curated text–video pairs capture each character’s actions, emotions, and behaviors, enabling the learned embeddings to serve as flexible building blocks for multi-character generation.

### 3.2 Cross-Character Augmentation (CCA)

While CCE ensures characters act authentically, training across mixed domains (cartoon and live-action) introduces a style delusion problem: characters may drift into unintended visual styles (e.g., Mr. Bean rendered as a cartoon, or Ice Bear appearing overly realistic), as shown in Figure [2](https://arxiv.org/html/2510.05093v1#S1.F2).

To preserve original styles while allowing cross-style interactions, we introduce Cross-Character Augmentation (CCA). This tackles a second non-coexistence challenge: in the training data, cartoon and real characters never appear together, and neither do their backgrounds.

Synthetic Cross-Domain Compositing.
Our intuition is that even imperfect synthetic co-occurrences can effectively guide the model toward more consistent, style-preserving generation. We therefore create augmented training clips by segmenting characters from source videos and pasting them into backgrounds from the opposite style domain. For example, Mr.Bean (live-action) may be placed in a cartoon Tom and Jerryscene as shown in the right part of Figure [3](https://arxiv.org/html/2510.05093v1#S3.F3).

Characters are segmented using SAM2 (Ravi et al., 2024), which handles both live-action and animation content. To ensure the quality and relevance of the synthetic compositions, we apply different filtering strategies. For live-action characters such as Mr. Beanand Young Sheldon, we perform reference-image matching to retain clips with accurate identity alignment. For cartoon characters, we leverage Gemini (Team, 2024a) for automated detection and filtering of characters.

The composited clips are then captioned with GPT-4o, which receives both the background source and the inserted character identities. Each caption is further enriched with explicit style tags ([scene-style:cartoon] or [scene-style:realistic]), providing the model with clear supervision for style control. The complete caption format becomes:

[Character: <name>], <action>.
[Character: <name>], <action>.
<scene-style>

Empirical Findings.
We observe that a small proportion of such augmented clips suffices to unlock robust cross-style composition. Excessive synthetic data, however, degrades realism and harms overall video quality. A detailed analysis is provided in the experiments section.

### 3.3 Training and Data

During fine-tuning, backbone parameters are frozen and only LoRA layers are updated, ensuring efficiency and reducing overfitting. We adopt rank-32 LoRA layers and train for 5 epochs with the Adam optimizer (learning rate 1e-4, batch size 64). Gradient clipping is applied for stability, and mixed-precision (FP16) training is used for efficiency.

Scenes and segments.
We curate a dataset comprising two cartoons and two live-action shows: approximately 9 hours of Tom and Jerry, 18 hours of We Bare Bears, 8 hours of Mr. Bean, and 46 hours of Young Sheldon. We standardize all videos by cropping out bottom text overlays (e.g., subtitles, credits) to prevent spurious language cues. Videos are segmented scene-by-scene into 5-second clips with the length of 81 frames, at 16 fps.

For each domain, we define the set of key characters: Mr. Bean  (Mr Bean), Tom and Jerry  (Tom, Jerry, Spike), We Bare Bears  (Ice Bear, Grizzly, Panda), and Young Sheldon  (Sheldon, Missy, Mary Cooper, George Cooper).

Figure: Figure 4: Comparison on multi-subject interaction. Results from SkyReel-A2 (Fei et al., 2025) (top row) and ours (bottom row).
Refer to caption: figure/images/comparison/multi_fishing.png

Figure: Figure 5: Comparison on single-subject generation. From top to bottom: results from VideoBooth (Jiang et al., 2024), DreamVideo, Wan2.1-I2V (Team Wan, 2025), SkyReel-A2 (Fei et al., 2025) and ours.
Refer to caption: figure/images/comparison/30_panda_karaok.jpg

## 4 Experiments

Figure: Figure 6: Ablation on caption format. From top to bottom: without tags, with [character] tag, with [scene-style] tag, and with both character and scene-style tags.
Refer to caption: figure/images/ablation_caption.png

### 4.1 Benchmarks

We evaluate our method using a comprehensive set of metrics along two dimensions. For overall video quality and temporal coherence, we adopt Consistency, Motion, Dynamic, Quality, and Aesthetic from VBench (Huang et al., 2024). To assess character-level consistency and interaction, we employ a vision–language model (VLM) and introduce four specialized metrics: Identity-P, Motion-P, Style-P, and Interaction-P. Specifically, we leverage Gemini-1.5-Flash (Team, 2024a) as the VLM backbone for these evaluations.

Video Quality and Temporal Consistency.
(1) Consistency evaluates the overall video-text consistency across frames computed by ViCLIP (Wang et al., 2023).
(2) Motion measures the level of smoothness of generated motions.
(3) Dynamic quantifies the degree of motion dynamics using RAFT (Teed & Deng, 2020).
(4) Quality measures the imaging quality, referring to the distortion (e.g., over-exposure, noise, blur) by the image quality predictor MUSIQ (Ke et al., 2021).
(5) Aesthetic evaluates the artistic and beauty value perceived by humans towards each video frame using the LAION aesthetic predictor.

Character Consistency and Interaction.

Identity-P evaluates how well the generated video preserves each character’s visual identity and distinctive features. The VLM assesses facial feature consistency, body proportions, characteristic attributes (e.g., Jerry’s mouse ears, Tom’s whiskers), and overall color scheme. A score of 10 indicates perfect identity preservation, where the character is immediately recognizable; a score of 1 indicates the character is completely unrecognizable.

Motion-P measures the authenticity of character-specific movements and behaviors relative to their canonical personality traits. The evaluation considers motion patterns (e.g., Jerry’s quick scurrying, Tom’s exaggerated sneaking), behavioral consistency, and expression of personality through movement. The VLM analyzes temporal sequences to assess alignment with the character’s known behavior patterns.

Style-P assesses the consistency of each character’s original artistic and visual style. This includes animation style (e.g., cartoon vs. realistic), aesthetic coherence with the source material, art direction fidelity, and rendering consistency. The VLM compares the generated video to its learned representation of the character’s canonical appearance and stylistic conventions.

Interaction-P evaluates the naturalness and plausibility of multi-character interactions. The assessment considers spatial relationships, timing and coordination, believability of reactions and responses, and physical dynamics between characters. For single-character videos, this metric evaluates interactions with the environment and scene elements.

### 4.2 Comparison

We benchmark our method against state-of-the-art video generation baselines, including two single-subject customization approaches—VideoBooth (Jiang et al., 2024) and DreamVideo (Wei et al., 2024)—as well as foundation image-to-video models Wan2.1-I2V (Team Wan, 2025) and SkyReels-A2 (Fei et al., 2025), both of which support single- and multi-character generation. For multi-subject customization specifically, we compare directly against SkyReels-A2.
Note that Wan2.1-I2V cannot directly generate videos from a character image. Thus, we first employ OmniGen (Xiao et al., 2025) to synthesize an image using the prompt and reference, which is then used as input to Wan2.1-T2V.

For single-subject evaluation, we generate 50 videos featuring 10 characters—five from cartoons (Tom, Jerry, Grizzly, Ice Bear, Panda) and five from live-action series (Mr. Bean, Sheldon, George, Mary, Penny).
For multi-subject evaluation, we generate 50 videos, each featuring 2–3 characters interacting within the same scene (noting that SkyReels-A2 supports fewer than three characters). These interactions span a wide range of scenarios, including inter-style (cartoon with real-life), intra-style (within cartoons or within real-life), inter-series (across different shows), and intra-series (within the same show). All reference images are included in the Appendix.

Figure [5](https://arxiv.org/html/2510.05093v1#S3.F5) shows the qualitative comparison on single subject video generation.
VideoBooth (Jiang et al., 2024) and DreamVideo (Wei et al., 2024) fail to preserve the visual identity of the reference character.
SkyReel-A2 (Fei et al., 2025) and Wan2.1-I2T (Team Wan, 2025) retain identity to some extent—though they struggle with facial details—but fail to synthesize character-faithful motions.
In contrast, our method consistently preserves visual fidelity and generates character-faithful motion.

Figure [4](https://arxiv.org/html/2510.05093v1#S3.F4) presents qualitative comparisons on multi-character interaction, both within the same style and across different styles. While SkyReel-A2 (Fei et al., 2025) can synthesize acceptable single-subject videos, it struggles with complex interactions across multiple characters, especially in multi-style settings. Although it can place several characters into a shared scene, the results often exhibit visual inconsistencies and unnatural interactions. In contrast, our method enables contextually coherent interactions without compromising character identity or native style.

Quantitative results across nine metrics are reported in Table [1](https://arxiv.org/html/2510.05093v1#S4.T1). Our method consistently outperforms prior approaches in both single- and multi-subject settings, demonstrating stronger identity preservation, faithful motion synthesis, and coherent style maintenance across diverse interaction scenarios. Additional comparison results are provided in the Appendix.

Figure: Figure 7: Ablation on augmentation data ratio. From top to bottom: 0%, 5%, 10%, and 20% augmentation.
Refer to caption: figure/images/augdata_ratio.jpg

**Table 1: Comparison of recent video generation models across evaluation dimensions. The first group of columns includes automatic evaluation metrics, while the last three report human evaluation scores. Bold indicates the best performance per column.**
|  |  | VBench Metrics (Huang et al., 2024) | VLM Metrics |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | Subject | Consistency | Motion | Dynamic | Quality | Aesthetic | Identity-P | Motion-P | Style-P | Interaction-P |
| VideoBooth (Jiang et al., 2024) | Single | 0.1287 | 0.9780 | 0.5094 | 0.6413 | 0.4896 | 4.45 | 3.72 | 5.43 | 4.44 |
| DreamVideo (ByteDance Team, 2025) | Single | 0.1851 | 0.9564 |  | 0.6270 | 0.5002 | 4.51 | 4.16 | 6.82 | 5.37 |
| Wan2.1-I2V (Team Wan, 2025) | Single | 0.0682 | 0.9827 | 0.6530 | 0.7192 | 0.5857 | 5.27 | 5.10 | 7.94 | 6.41 |
| SkyReels-A2 (Chen et al., 2025a) | Single | 0.1469 | 0.9782 | 0.7843 | 0.7225 | 0.5850 | 6.17 | 4.55 | 7.82 | 6.78 |
| Ours | Single | 0.1893 | 0.9836 | 1.0000 | 0.5763 | 0.5967 | 6.12 | 5.41 | 8.06 | 7.24 |
| SkyReels-A2 (Chen et al., 2025a) | Multiple | 0.1314 | 0.9650 | 0.9787 | 0.7140 | 0.5371 | 6.17 | 4.55 | 6.28 | 4.94 |
| Ours | Multiple | 0.1833 | 0.9842 | 0.98555 | 0.6855 | 0.5813 | 6.48 | 5.50 | 7.26 | 5.22 |

**Table 2: Effect of Caption Formatting. We compare different caption formats, with and without structured tags, across multiple evaluation dimensions. Our full formatting with both [character] and [scene-style] tags achieves the best performance.**
|  | VBench Metrics (Huang et al., 2024) | VLM Metrics |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Caption Format | Subject-C | Background-C | Motion-S | Dynamic | Quality | Identity-P | Motion-P | Style-P | Interaction-P |
| No Tag (Baseline) | 0.8892 | 0.9136 | 0.9812 | 1.0000 | 0.6535 | 7.02 | 5.90 | 6.83 | 4.28 |
| w/o [scene-style] | 0.8668 | 0.8980 | 0.9754 | 1.0000 | 0.6353 | 7.31 | 5.80 | 6.95 | 4.47 |
| w/o [character] | 0.8758 | 0.9101 | 0.9759 | 1.0000 | 0.6938 | 7.33 | 5.42 | 6.80 | 4.47 |
| w/ Both (Ours) | 0.8530 | 0.8997 | 0.9747 | 1.0000 | 0.6588 | 7.35 | 5.80 | 6.95 | 5.30 |

**Table 3: Effect of Synthetic Data Augmentation. We vary the proportion of synthetic videos relative to the original dataset and evaluate across the same dimensions used in Table [1](https://arxiv.org/html/2510.05093v1#S4.T1).**
|  | VBench Metrics (Huang et al., 2024) | VLM Metrics |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Ratio | Subject-C | Background-C | Motion-S | Dynamic | Quality | Identity-P | Motion-P | Style-P | Interaction-P |
| 5% | 0.8739 | 0.9082 | 0.9826 | 0.9000 | 0.6836 | 7.67 | 6.03 | 7.15 | 4.83 |
| 10% | 0.8812 | 0.9151 | 0.9853 | 0.9500 | 0.6955 | 8.33 | 6.72 | 7.33 | 4.78 |
| 20% | 0.8442 | 0.8905 | 0.9779 | 1.0000 | 0.6728 | 8.30 | 7.10 | 7.08 | 3.90 |

### 4.3 Ablation study

We conduct ablation studies to investigate how different captioning strategies influence the model’s ability to learn and ground each character as a distinct concept. Additionally, we analyze how varying the ratio of augmentation data affects the mitigation of style delusion.

Captions Formats.
To evaluate the role of structured captions, we compare models trained with standard free-form captions against those trained with captions augmented by our proposed tags [scene-style] and [character]. The structured format provides explicit grounding of both scene attributes and character identities. Figure [6](https://arxiv.org/html/2510.05093v1#S4.F6) illustrates a representative example of our video–caption pairs, showing how the tags enable more faithful alignment between visual entities and textual descriptions. The second row (without the [scene-style] tag) shows a shift from a cartoon scene to a realistic one. The third row (without the [character] tag) fails to include Mr. Bean in the generated video. In contrast, our method, using both tags, produces consistent scenes and correctly includes all characters.

Augment Data Ratio.
We analyze the effect of incorporating our composited dataset (Section LABEL:sec:augmentation) at different mixing ratios. Specifically, we vary the proportion of synthetic videos relative to the original curated dataset and evaluate the impact on performance. This experiment highlights how synthetic cross-character interactions contribute to improved generalization and robustness in multi-character video generation.
Figure [7](https://arxiv.org/html/2510.05093v1#S4.F7) illustrates a representative example. Both the baseline (0% augmentation) and the 5% augmentation setting generate a cartoon-style scene. While the 5% setting correctly produces a cartoon-style Mr. Bean, the 0% setting fails to preserve identity, instead generating a random cartoon character—possibly resembling one from the We Bare Bears,series. At 10% augmentation, the model successfully generates a realistic Mr. Bean with plausible interaction within the scene. However, at 20%, although Mr. Bean’s appearance remains realistic, the interaction becomes less coherent, likely due to the overuse of synthetic data.

## 5 Discussion

Despite the effectiveness of our framework in enabling controllable multi-character video generation, it comes with several limitations. Most notably, our approach relies on explicit identity annotations and LoRA fine-tuning. As a result, introducing a new character—whether from a different show or an unseen domain—requires retraining or fine-tuning the model. This limits scalability in open-world settings, where users may wish to generate videos with arbitrary or user-defined characters.

Furthermore, while our captioning and augmentation strategies mitigate style delusion and enable robust character disentanglement, the model still exhibits occasional failure cases in highly complex interaction scenes, especially when multiple characters with overlapping appearances or motion patterns are present.

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## Appendix A Use of LLM

We used a large language model (GPT-4) to assist with grammar correction and language refinement throughout the writing process. Specifically, the model was employed to polish sentence structure, improve clarity, and ensure consistency in technical terminology. No content or experimental results were generated by the model; all scientific contributions and analysis were conducted by the authors.

## Appendix B Dataset

Character Distribution. Based on the character distribution analysis across 52,792 video clips from four TV series, there is significant variation in character
prominence. Sheldon dominates the Young Sheldondataset, appearing in 67.2% of the 24,447 clips, making him the most frequently featured
character overall with 16,440 occurrences. The We Bare Bearsshows more balanced character distribution, with Grizzly (57.9%), Panda
(52.8%), and Ice Bear (46.1%) all appearing in roughly half of their 16,762 clips. Mr. Bean demonstrates the highest character
consistency, appearing in 94.7% of his show’s 6,662 clips, reflecting the show’s single-protagonist nature. In contrast, Tom and Jerryexhibits a hierarchical character presence, with the titular characters Tom (70.9%) and Jerry (48.8%) appearing much more frequently than
supporting characters like Spike (22.6%), Tuffy (12.7%), and Quacker (8.0%). The Young Sheldonseries shows the most diverse character
representation with eight tracked characters, though secondary characters like Mary Cooper (29.8%) and George Cooper (19.5%) appear
significantly less than the protagonist. Overall, the data reveals that protagonist-centered shows (Mr. Bean, Young Sheldon) maintain
high main character presence, while ensemble shows (We Bare Bears) and classic animation (Tom and Jerry) display more varied character
distribution patterns.

Figure: Figure 8: Character distribution in our dataset. The bar chart shows the number of appearances of each character in the video-caption pairs.
Refer to caption: figure/images/character_distribution_vertical_bar_hq.png

Captioning Prompt to GPT-4o.
We provide GPT-4o with 10 frames and the corresponding transcript, along with carefully designed instructions to ensure precise and consistent character references. Prompts for the four shows are shown below.

Figure: Figure 9: Reference images of 5 cartoon characters including Panda, Grizzly, Ice Bear, Tom and Jerry, and 5 realistic characters including Mr. Bean, George Cooper, Mary Cooper, Penny and Sheldon.
Refer to caption: figure/images/reference.jpg

Figure: Figure 10: Comparison on multi-subject interaction. Results from SkyReel-A2 (Fei et al., 2025) (top row) and ours (bottom row).
Refer to caption: figure/images/comparison/multi_tech_startup.png

## Appendix C Experiments

Reference images. We provide 10 reference images together with their corresponding masks for evaluating VideoBooth (Jiang et al., 2024), DreamVideo (Wei et al., 2024), Wan2.1 (Team Wan, 2025), and SkyReel-A2 (Fei et al., 2025), as illustrated in Figure [9](https://arxiv.org/html/2510.05093v1#A2.F9).

Single-Subject Comparison. We provide additional qualitative comparisons with VideoBooth (Jiang et al., 2024), DreamVideo (Wei et al., 2024), Wan2.1 (Team Wan, 2025), and SkyReel-A2 (Fei et al., 2025) in Figure [12](https://arxiv.org/html/2510.05093v1#A3.F12), [13](https://arxiv.org/html/2510.05093v1#A3.F13), and [14](https://arxiv.org/html/2510.05093v1#A3.F14). These examples further demonstrate our method’s ability to preserve subject identity and produce coherent motion across diverse scenarios.

Multi-Subject Comparison. We present additional qualitative comparisons with SkyReel-A2 (Fei et al., 2025) in Figure [10](https://arxiv.org/html/2510.05093v1#A2.F10). The results demonstrate our method’s capability to synthesize natural and coherent interactions among multiple characters while faithfully preserving their identities, characteristic behaviors, and distinctive visual styles. In comparison to SkyReel-A2, our approach yields fewer style inconsistencies and artifacts, producing more realistic and engaging multi-subject generations.

Figure: Figure 11: Comparison on single-subject generation. From top to bottom: results from VideoBooth (Jiang et al., 2024), DreamVideo, Wan2.1 (Team Wan, 2025), SkyReel-A2 (Fei et al., 2025) and ours.
Refer to caption: figure/images/comparison/marry.jpg

Figure: Figure 12: Comparison on single-subject generation. From top to bottom: results from VideoBooth (Jiang et al., 2024), DreamVideo, Wan2.1 (Team Wan, 2025), SkyReel-A2 (Fei et al., 2025) and ours.
Refer to caption: figure/images/comparison/10_tom_milk.jpg

Figure: Figure 13: Comparison on single-subject generation. From top to bottom: results from VideoBooth (Jiang et al., 2024), DreamVideo, Wan2.1 (Team Wan, 2025), SkyReel-A2 (Fei et al., 2025) and ours.
Refer to caption: figure/images/comparison/jerry_sleep.jpg

Figure: Figure 14: Comparison on single-subject generation. From top to bottom: results from VideoBooth (Jiang et al., 2024), DreamVideo, Wan2.1 (Team Wan, 2025), SkyReel-A2 (Fei et al., 2025) and ours.
Refer to caption: figure/images/comparison/01_mrbean_eat.jpg

## Peer Reviews

confidence: 4
contribution: 4
initial_rating: 6
presentation: 3
questions: 1.  **On Data Requirements**: Could you please clarify the data requirements more explicitly? The paper implies a single fine-tuning on a mixed dataset. Does this mean to add a new character from a new TV show, one must re-run the entire fine-tuning process on the combined old and new data? What is the approximate training time (e.g., in GPU-hours) for the reported 5-epoch fine-tuning on the 81-hour dataset?
2.  **On Evaluation Metrics**:
    *   (a) Could you justify the decision to exclusively use VLM-based metrics for identity preservation over established methods like face recognition ID similarity, at least for the human characters (Mr. Bean, Young Sheldon's cast)? A comparison showing the VLM's superiority would strengthen your metric choice.
    *   (b) Crucially, what protocol was used to ensure the VLM evaluation is deterministic and reproducible? Please provide the exact prompts, model version, and parameter settings (especially `temperature`) used to query Gemini-1.5-Flash for the Identity-P, Motion-P, Style-P, and Interaction-P scores in the appendix.
3.  **On Generalizability**: To substantiate the "model-agnostic" claim, could you provide any results or insights, even preliminary, from applying your CCE and CCA framework to a different base T2V model?
4.  **On Inconsistent Ablation Results**: Could you please clarify and unify the results presented in Tables 1, 2, and 3? Specifically, please ensure that the results for the same experimental setup are consistent across all tables and explain the counter-intuitive findings in Table 2 (e.g., the drop in `` `Subject-C` ``).
5.  **On CCE's Mechanism**: The term "Cross-Character Embedding" suggests a specific learned representation. Can you clarify whether your method learns an explicit, separable embedding for each character, or if CCE is more accurately described as a structured prompting technique that influences the text-conditioning of the frozen T2V model?
soundness: 3
strengths: *   **Problem Formulation and Significance**: The paper formulates a compelling and significant research problem: enabling characters from different "universes" to interact naturally in generated videos. This is a natural evolution of personalized generation and has high potential for creative applications.
*   **Novel Methodology**: The proposed framework is original in its combination of two distinct ideas to solve two well-defined problems. CCE (via prompt structure) tackles the non-coexistence of characters, and CCA (via synthetic compositing) addresses the style delusion problem. This two-pronged approach is elegant and shown to be effective.
*   **Strong Empirical Results**: The qualitative results are visually impressive and clearly demonstrate the superiority of the proposed method over existing approaches, which either fail to maintain identity or cannot produce coherent interactions. The quantitative results, despite the aforementioned issues with the ablation tables, generally show a strong performance lead, especially on the task-specific VLM metrics.
*   **Benchmark Contribution**: The introduction of a dedicated benchmark for multi-character interaction, including a new suite of VLM-based metrics tailored to character identity, motion, style, and interaction, is a substantial contribution in its own right. It provides a more meaningful way to evaluate models on this task than standard metrics alone.
summary: This paper introduces a novel framework for generating videos that feature multiple characters from different fictional universes (e.g., cartoons and live-action shows) interacting with each other. The authors identify two primary challenges: 1) the "non-coexistence" of characters in training data, and 2) "style delusion," where characters' visual styles blend undesirably. To address these, they propose a two-part solution. First, **Cross-Character Embedding (CCE)**, a prompt engineering strategy that uses structured captions of the form `` `[Character: <name>], <action>` `` to disentangle character identity and behavior. This allows the model to learn character-specific traits from their respective source videos and compose them at inference time. Second, **Cross-Character Augmentation (CCA)**, a data augmentation technique that synthetically creates training examples of cross-style interaction by segmenting characters from one domain and pasting them into scenes from another. These augmented clips are captioned with an additional `` `[scene-style: <style>]` `` tag to help the model preserve stylistic integrity. The authors fine-tune a large-scale text-to-video model (Wan2.1) on a curated 81-hour dataset and demonstrate through extensive experiments that their method significantly outperforms existing baselines in identity preservation, interaction quality, and style consistency. They also introduce a new benchmark and a set of VLM-based evaluation metrics for this specific task.
weaknesses: *   **Scalability and Data Dependency**: The method's primary weakness is its reliance on fine-tuning using a large corpus of video data for a pre-defined set of characters. As implied in Section 3.3, each new character universe (e.g., a TV show) requires collecting hours of video footage and undergoing an expensive fine-tuning process. This makes it difficult to scale to new, arbitrary characters in an open-world setting. As acknowledged by the authors, this is a significant limitation.
*   **Lack of Human Evaluation and Unjustified Metric Choices**: For a task where success is highly subjective (e.g., "authenticity", "plausibility"), the absence of a human study is a major weakness. The paper instead introduces VLM-based metrics but fails to justify why established identity preservation metrics (e.g., face recognition similarity) were not used or compared against, especially for the human-like characters. While VLM evaluation is innovative, its reliability is questionable without proper protocol.
*   **Questionable Reproducibility of VLM-based Evaluation**: The use of a VLM as a core evaluation tool introduces significant reproducibility concerns. The output of VLMs can be stochastic due to parameters like `temperature`. The paper fails to describe the protocol used to ensure deterministic and reproducible scores. Key details are missing: Was `temperature` set to 0? Were scores averaged over multiple runs? What were the exact prompts used? Without this information, the quantitative results in Table 1, 2, and 3 are not fully credible.
*   **Unverified Claim of Model-Agnosticism**: The paper claims its framework is "model-agnostic" (Section 3.1) but provides no empirical evidence. All experiments are conducted by fine-tuning a single base model (Wan2.1-T2V-14B). Without applying the CCE and CCA framework to at least one other distinct T2V backbone, this claim of generalizability remains unsubstantiated.
*   **Clarity and Consistency of Ablation Studies**: The quantitative results in the ablation section (Tables 2 and 3) are poorly presented. There are numerical inconsistencies for what should be identical experimental conditions across different tables. For example, the results for the full model ("Ours" in Table 1, "w/ Both" in Table 2, and "10%" in Table 3) all report different scores. This makes it impossible to confidently assess the individual contributions of the proposed components. This must be fixed.

---

confidence: 4
contribution: 2
initial_rating: 4
presentation: 2
questions: 1.Can the method handle unseen characters or styles at inference time?

2.What would happen if GPT-4o/Gemini were replaced with open-source models (e.g., LLaVA, Qwen2-VL)?

3.How is the proposed CCE/CCA architecture implemented and trained?
soundness: 2
strengths: 1. The idea of character mixing is creative and intuitively interesting, which could inspire further exploration in cross-style or cross-domain video generation.

2. The authors contribute a dataset with structured captions for multi-character interaction videos, which may be useful for future research.
summary: This paper introduces a new task termed Character Mixing, aiming to generate multi-character videos where characters from different IPs or styles appear and interact in the same scene. The authors construct a dataset by mixing existing videos and captions with the help of large language and vision models, and design two modules (CCE and CCA) to align cross-character and cross-style information. Experiments on several cartoon characters are reported.
weaknesses: 1.Low problem significance.
While entertaining, the problem does not address a clear or impactful research challenge. It is more of a creative application than a fundamental scientific question. The motivation for why character mixing matters for the video generation community is weak.

2.Limited methodological novelty.
The proposed CCE and CCA modules mainly rely on prompting and data augmentation rather than introducing new modeling or learning principles. The improvements largely stem from the underlying base model's(Wan2.1 14B used, smaller size or other open-source model should be presented to support model-independent claim)  capability rather than the proposed method itself.

3.No formalization or mathematical clarity and training details.
The paper lacks any formal task definition, notation, or training objective. Without clear training details, it is difficult to reproduce and evaluate the soundness of the approach.

4.Limited scalability and generalization to new characters.
The reliance on explicit character-level annotation and LoRA-based fine-tuning means adding a new character (especially from unseen domains) requires re-training or substantial data preparation (highlighted in Discussion, Page 9). This approach does not scale gracefully for open-world or user-specified characters.

5.Missing analysis and discussion.
There is little exploration of failure cases, interaction quality (e.g., temporal consistency, occlusion handling), or computational cost. The paper reads more like a demo than a scientific study.

---

confidence: 3
contribution: 2
initial_rating: 4
presentation: 3
questions: 1. How were baselines adapted. Did you fine tune SkyReels A2, Wan2.1 I2V, or other baselines on the same eighty one hour dataset with LoRA style adapters, or are they evaluated zero shot. Please clarify to ensure a fair comparison.

2. Can you quantify failure modes. The discussion notes occasional breakdowns in highly complex multi character scenes with overlapping motion patterns.
soundness: 2
strengths: 1. The paper tackles a concrete and highly visible capability gap in current video generation systems. Existing models can often render a single customized subject, but coherent multi-character interaction across different shows and even across cartoon versus live action domains remains very brittle.

2. The authors curate a reasonably large, annotated, behavior-aware dataset, and define evaluation metrics that target identity, motion, style, and interaction quality in multi-character settings.

3. Qualitative demos show convincing multi-character interactions that typical text-to-video systems struggle with.
summary: The authors identify two core challenges: non-coexistence, meaning some characters never appear together in training data, and style delusion, meaning cartoon and live action styles bleed into each other. To address this, they propose Cross Character Embedding, which uses structured captions of the form [character: name], action to disentangle identity and behavior, and Cross Character Augmentation, which pastes segmented characters into foreign style backgrounds to simulate cross-universe co-occurrence while preserving each character’s native look.
weaknesses: 1. Baseline fairness is underspecified. The proposed model is LoRA fine tuned on an 81 hours character specific dataset, whereas baselines may be evaluated mostly zero shot. This makes it hard to attribute the gains to CCE or CCA rather than just stronger task specific tuning.

2. Generalization is narrow. All results focus on about ten characters from four shows. The paper does not show that the approach scales to arbitrary new identities or unseen characters without new fine tuning.

---

confidence: 3
contribution: 2
initial_rating: 6
presentation: 3
questions: How many A100 GPUs were used and how long does it took for training?

Are you planning to open-source the code and dataset?
soundness: 3
strengths: A key strength of this paper lies in its clear motivation, to bridge the gap between distinct fictional universes by enabling natural, style-consistent multi-character interactions. This being imaginative and well-grounded in practical generative modeling challenges. The authors present a thoughtfully designed data composition strategy, curating a dataset that balances diversity across cartoons and live-action domains while maintaining detailed character and style annotations. 

This well-structured dataset, combined with the Cross-Character Embedding and Augmentation techniques, provides a solid foundation for learning both behavioral and stylistic nuances. Overall, the study is well-organized, and the presentation neatly ties together motivation, method, and results into a coherent and convincing.
summary: This paper introduces a new framework for enabling natural interactions between multiple characters from different visual domains, such as mixing Mr. Bean with Tom and Jerry using text-to-video (T2V) generation. The authors address two key challenges: non-coexistence, where characters from separate shows never appear together in training data, and style delusion, where mixed-style characters lose their original visual fidelity. To overcome these, they propose Cross-Character Embedding (CCE), which learns disentangled identity and behavioral representations from multimodal sources through structured character–action captions, and Cross-Character Augmentation (CCA), which generates synthetic co-existence data by compositing characters across domains while preserving native styles. As a result, the paper shows improvement compared to the baselines.
weaknesses: [Major]
While the paper presents an impressive dataset and compelling demonstrations, its technical contribution beyond data construction and fine-tuning remains relatively incremental. The proposed Cross-Character Embedding (CCE) and Cross-Character Augmentation (CCA) modules, though effective, largely extend existing ideas of prompt-based disentanglement and synthetic compositing rather than introducing fundamentally new architectural or generative mechanisms. 

The framework heavily relies on caption engineering, LoRA-based adaptation, and GPT-assisted annotation which approaches that are conceptually straightforward and depend more on large-scale data quality than algorithmic novelty. Consequently, while the work excels in implementation and application scope, its methodological innovation is modest compared to the scale of its dataset and the strength of its empirical results. 

[Minor]
Some references related to video personalization are missing. ToonCrafter [1] proposed a fine-tuning technique for adapting video diffusion models to general cartoon domains, while AnyMoLe [2] introduced a video fine-tuning framework for generating motion-consistent videos of a single character.

There is typo in L464 (Section ??).

[1] ToonCrafter: Generative Cartoon Interpolation via Diffusion Models. Liu et al., SIGGRAPH ASIA, 2024.

[2] AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models. Yun et al., CVPR, 2025.


Decision: Reject