DreamSwapV: Mask-guided Subject Swapping for Any Customized Video Editing
Authors: Weitao Wang
Zichen Wang
Hongdeng Shen
Yulei Lu
Xirui Fan
Suhui Wu
Jun Zhang
Haoqian Wang
Hao Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Video Editing
Video Customization
Video Inpainting
Diffusion Transformer
Computer Vision
Primary area: generative models
TL;DR: We present a mask-guided, subject-agnostic framework dedicated to end-to-end generic subject swapping for video customization, achieving state-of-the-art results in the video subject swapping task.

Abstract: With the rapid progress of video generation, demand for customized video editing is surging, where subject swapping constitutes a key component yet remains under-explored. Prevailing swapping approaches either specialize in narrow domains—such as human-body animation or hand-object interaction—or rely on some indirect editing paradigm or ambiguous text prompts that compromise final fidelity. In this paper, we propose DreamSwapV, a mask-guided, subject-agnostic, end-to-end framework that swaps any subject in any video for customization with a user-specified mask and reference image. To inject fine-grained guidance, we introduce multiple conditions and a dedicated condition fusion module that integrates them efficiently. In addition, an adaptive mask strategy is designed to accommodate subjects of varying scales and attributes, further improving interactions between the swapped subject and its surrounding context. Through our elaborate two-phase dataset construction and training scheme, our DreamSwapV outperforms existing methods, as validated by comprehensive experiments on VBench indicators and our first introduced DreamSwapV-Benchmark.

Published as a conference paper at ICLR 2026
DREAMSWAPY. MASK-GUIDED SUBJECT SWAPPING FOR ANY CUSTOMIZED VIDEO EDITING
Weitao Wang .2 Zichen Wang2 Hongdeng Shen? Yulei Lu² Xirui Fan?
Suhui Wu Jun Zhang Haoqian Wang'* Hao Zhangat 'Tsinghua University ByteDance
wangut23emails wttpaaai...ssssnnnnng.e..eeme www.sssa..d.....................
ABSTRACT
With the rapid progress of video generation, demand for customized video edit- ing is surging, where subject swapping constitutes a key component yet remains under-explored. Prevailing swapping approaches either specialize in narrow do- as human-bou body animation or hand-object interaction or rely on some indirect editing paradigm ambiguous text prompts that compromise final fidelity. In this paper, we propose DreamSwapV, mask-guided, subject-agnostic, end-to-end framework that swaps any subject in any video for customization with a user-specified mask and reference image. To inject fine-grained guidance, we introduce multiple conditions and dedicated condition fusion module that in- tegrates them efficiently. In addition, an adaptive mask strategy is designed to accommodate subjects of varying scales and attributes, further improving inter- actions between the swapped subject and its surrounding context. Through our elaborate two-phase dataset construction and training scheme, our DreamSwapV outperforms existing methods, as validated by comprehensive experiments on VBench indicators and our first introduced DreamSwap V-Benchmark.
INTRODUCTION
The recent development of video generation technologies is enabling a broader audience to engage in customized content creation, also stimulating demand for editing real generated videos. Users increasingly desire to introduce specific subjects into videos following the original motion trajectory, necessitating the video subject swapping task. Given source subject in the video and target subject to be inserted, the ideal outcome is that the target is seamlessly integrated into the original video, preserving its own appearance details, following the source motion, and interacting naturally with the surrounding context.
However, existing video editing methods exhibit critical limitations: On one hand, domain-specine approaches are confined to particular subjects: animation techniques like MagicAnimate (Xu et al.) 2024b) and Animate Anyone (Hu et al. (2025)) only support swapping human characters; Human- Object Interaction (HOI) methods like AnchorCrafter (Xu et al.) (024c) and DreamActor-HI (Wang) ctt al. 2025) are limited to specific scenarios (e.g. livestreaming, E-commerce) for swapping hand- held objects; thereby restricting their broader applicability. On the other hand, general purpose video editing methods also face several challenges: (1) Tuming-free techniques (Ku et al. 2024 Yang et al.) 2025a) manipulate attention features to swap subjects, but struggle to restore fine details and largely ignore interactions with subjects; (2) Tuning-based methods inject the target subject either via text prompts like VideoPainter (Bian al. (025) learned LoRA (Hu et al. 2022) from several sub- ject concepts like VidcoSwap (Gu et al.) 2024); the former lacks detail fidelity, whereas the latter computationally demanding and indirect reference injection; (3) Recently, unified video cus- tomization frameworks Jiang et al., 2025 Hu et al., 2025b) are emerging, aiming for versatility across multiple customization tasks, including video-driven subject swapping. Yet their all-in-ond design sacrifices identity consistency and interaction realism in the subject swapping scenario.
general-purpose
To address these limitations, we present DreamSwapV, a mask-guided and subject-agnostic frame- work for video subject swapping. Given source video, a user-specified mask localizing the source subject, and a reference image of the target subject, we can perform end-to-end swapping of any
"ooresponning Authors. Projecct Leader.

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BreanShapl/
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Human Gorment
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Figure 1: Our DreamSwapV swaps any subject in any video given a mask and reference image.
subject any video. Unlike conventional editing paradigms that focus on regenerating or editing an external object into the scene, recast swapping
inpainting task: encourage the model to recover the appearance of the target subject as it inherently belonged within the missing masked region of the source video, yielding direct and intuitive training and inference procedure.
Specifically, we first construct a task-oriented dataset based on HumanVIL (Wang et al., 2024c and introduce auxiliary conditions (mask, pose, 3D hand, etc.) for better video control. A dedicated con- dition fusion module then effectively integrates these conditions for the model's better understanding of both video content and task objectives. Furthermore, an adaptive mask strategy accommodates subjects of varying scales and attributes, mitigating shape leakage while enhancing subject-context interaction. Lastly, we establish
the first benchmark tailored to video subject swapping and evaluate our method along with 5 indicators inherited from VBench (Huang al. 2024). confirming the robustness and superior performance of DreamSwapV.
Our contributions are summarized as follows:
We propose DreamSwapV, the first end-to-end framework dedicated to the video subject swapping, specifically focusing on generic subject-context interaction.
Our novel condition fusion module and adaptive mask strategy enable handling diverse subjects, enhancing contextual understanding, and improving video realism.
Through two-stage dataset construction and training scheme, we outperform existing methods on VBench indicators and first introduced DreanSwapV-Benchnark
2 RELATED WORK
2.1 VIDEO GENERATION AND EDITING
Video generation is pivotal component within the Artificial Intelligence Generated Content (AIGC) domain, realizing human creativity in the video medium. The field has undergone rapid evolution.
from initial GAN-based methods (Vondrick al., 2016 Tulyakov al. 2018), through subsequent
diffusion models (Yang et al., b). to the current state-of-the-ant art Diffusion Transformers (DiT)
(Peebles Xie 2023p. At present, leading commercial models (Keling) 2025 Hailuo 2025 Vidu
2025) and open- source models (Yang et al.] 2024 Kong et al. 2024) can leverage billion-scale architectures to generate high-fidelity videos at 720p- 1080p resolutions.
Concurrently, editing techniques for real or generated videos are also advancing vigorously. Tune- V-Video (Wu et al., 2023) pioneers video editing with latent diffusion models via one-shot tuning. followed by (Qi et al. 22023 Cong et al. [023 Yang et al. (202dd), enhancing capabilities in appear- ance detail, temporal consistency and motion control. With the advent of video foundation models, an increasing number of editing methods are based video diffusion models (Gu et al. (2244) Yang ctt al. 2025a) or DiT (Bian et al. 2025 Tu et al. 2025), providing more precise and diverse video DiT model (Wan al., 2025) and adapts it specifically to the video subject swapping task, with our condition fusion module injecting additional conditioning information and carefully designed training scheme.
editing. Our DreamSwapV builds upon the cutting-edge Wan2.1-12V-1
2.2
VIDEO CUSTOMIZATION
The freedom and creativity of personalized content creation facilitates the development of video cus- tomization, which can be broadly classified into two paradigms. Subject concepts-driven methods leam subject-specific adapters (Houlsby et al. 2019 or LoRA modules (Hu et al. 2022) from multi- ple reference images, embedding the subject concepts into the text space. DreamBooth (Ruiz al., 2023) and Textual Inversion (Gal et al. 2022) pioneer this approach in the image domain and later extend video customization by methods like (Chefer et al. 2024 Wu al. 2025
Tiang et al.
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2024
While effective for identity preservation, the requirement of per-subject finetuning hampers real-time use, motivating the emergence of end-to-end methods.
End-to-end customization typically employs additional conditioning network to inject user- specified information, similar to the idea of ControlNet (Zhang et al. 2023). Animation techniques (Hu et al. 2025a) design ReferenceNet specifically for injecting detailed character appearance.
Unified video customization frameworks (Jiang et al., (025 Hu et al. (025b) broaden the scope to encompass comprehensive customization capabilities, including text-to-video (T2V), reference-to- video (R2V), video-to-video (V2V) and masked-videooto-vvdde (MV2V).
2.3 VIDEO SUBJECT SWAPPING AND INPAINTING
Video subject swapping is a derived sub-task of video customization, focusing on subject-driven editing and masked-video-to-vidde (MV2V), which has garnered significant attention due to rising user demand. Some general-purpor video editing methods (Ku et al. (0224) Yang et al.) 2025a Gu
al. 2024) inherently support video subject swapping, which operate in a regenerating or editing paradigm, rather than video inpainting.
Video inpainting (Xu et al.) 2019 Zhou al. 2023) itself is long-established research field, orig- inally concerned with restoring missing or damaged content in videos. Crucially, when the masked region represents an unknown external subject, video inpainting techniques can effectively recover this novel object, thereby achieving the functional goal of video subject swapping- demonstrated by VideoPainter (Bian et al.) 2025) and the MV2V capabilities unified customization frameworks (Jiang et al., [025 Hu et al.) 2025b). Our DreamSwapV directly inherits and builds upon this core insight, by training a dedicated, end-to-end model specifically designed to achieve generic and high- quality video subject swapping in video impainting manner.
3 METHOD
3.1 TASK OVERVIEW
As fully discussed above, we cast video subject swapping as special application of video inpaint- ing: a user-specified mask m of the first frame delimits a region to be swapped, and corresponding reference image r supplies the appearance of the new subject. Under this setting, the task can also transfer to (i) standard video inpainting when the reference is absent, or (ii) video addition when the masked region does not contain pre-existing subject. In this work, we mainly focus the canon- ical video subject swapping mask-namelyy the mask encloses a single subject and the reference depicts another subject of similar category.
Data Filtering and Preprocessing. To construct dataset tailored for our task, we leverage Human- VID (Wang al. 2024c) the foundational resource. Beyond its human-centric focus, Human VID offers extensive coverage of human-object interactions and diverse subject categories, aligning with our requirement for generic subject swapping.
Then we establish a rigorous data processing pipeline (video subject caption + per-frame subject mask tracking quality filtering = distribution standardization) to collect our final pre-training dataset. TikTok- Tok--VM--BB is employed to caption prominent subjects in each vidco of Human VID, followed by TrackingSAM (Cheng al.) 2023) performing per-frame tracking and segmentation of each subject. The obtained subject mask sequences then undergo strict quality filtering based on three criteria: (i) the mask area ratio, (ii) the temporal coverage (of total frames), and (iii) the motion amplitude (displacement across frames). To prevent our model from being overly biased towards any specific subject category, we further standardize the distribution of subjects in the dataset by filtering out excessively repetitive instances, ensuring balanced diversity of subject categories.
Following these steps, we finally construct our pre-training dataset containing 8160 videos and 16219 subject instances (4701 humans 1045 garments 5477 small objects 4996 large ob- jects Re :: 0.2 1: 1, where small objects include highly human-related items like accessories and handheld objects, and large objects include other items like furniture, buildings, and background).
Pose and 3D hand sequences are detected together by DWPose (Yang et al., 2023) and Hamer PPallkoos et al. 2024) per-frame and stored as auxiliary conditions required by Sec. 3.2. At last, each sample comprises (i) the raw video V = {vr) (ii) pose & 3D hand sequences P (p: [p and (iii) per-subject masks M (m²) 1t=1 where means the specifie subject.
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video
mask
noisy
noise
reference
Adaptive Mask Strategy
Training
3D VAE
Inference
Result
Condition Fusion
X Blocks
self- attn
DiT
dlowssample
Loss
agnostic
mask
3D VAE
lafent
concet
predicted noise
poso & 3D hand
space
bright widhh
Trame
leamable
= Lipe + Lyw
Figure 2: Overview of our framework. We first build a pre-training dataset containing video, raw mask, pose and 3D-hand sequences (Sec. B.I.. Our condition fusion module then integrates these signals with strict spatial-temporal alignment (Sec. (.2).. To improve subject-context interaction
and enable general subject swapping, introduce adaptive mask strategy (Sec. 3.3). Finally,
with a two-phase training scheme and additional technical implementations (Sec. 3.4 we deliver sfate-of-the-ars swappin- results at inference time.
Training and Inference. During training, the reference image is collected by r' ='= = i is a randomly selected frame and @ denotes element-wise product, i.e., extracting the subject marked by the mask from the video frame as the training reference image. The model thus trained to recover the original video by inpainting the masked region using this extracted reference image, encouraging faithful appearance recovery and seamless interaction with the scene, which can be expressed as follows:
m,, where
Loss = Lpe (V. V"), V = ff ..P.P
where Ltpt represents the pre-trained loss, whose final calculation will be supplemented in Sec. and ʃa depicts the recovering procedure of our model with weight 0.
At inference time, the model treats an external reference image r as if it were the subject originally extracted from the mask region, leveraging recovering capability learned during training. Thus we can obtain the customized video V' after our model's subject swapping: = =o P. P,))). The whole process is overviewed in Figure
3.2 CONDITION FUSION MODULE
The subject swapping task inherently requires the model to distinguish between regions requiring to be swapped and those be preserved, which is relatively straightforward to learn with the binary mask serves as a key condition. Building on this, the more challenging issues are (i) accurately recovering the motion trajectory of the swapped subject, and (ii) handling the mask boundary inter- actions between the swapped subject and its surrounding context.
For dynamic subjects (e.g., humans and animals), self-occlusion frequently occurs during move- ment, necessitating the pose estimation as an additional condition to provide the subject's motion information. For static subjects (e.g., objects), the motion pattern from camera movement or nal forces is simpler, and usually can be inferred from mask shape changes. However, the realism of interactions between these objects and external forces (e.g- hand manipulations) also critically introduce 3D hand estimation
impacts visual quality. To better handle these interactions, complementary signal, providing hand-object interaction information. The pose and 3D hand esti- mation are combined unified temporal sequence P to offer their auxiliary control.
Along with the necessary mask sequence M the masked video sequence A = V V (1-MF) (often referred to agnostic) and the reference image r", we incorporate four additional conditions along with the noisy video input. Efficiently coupling this multi-modal information is essential to overall performance, where address this challenge through dedicated condition fusion module.

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CLIP
DiT
& other
cross-attn
conditions
(b) Cross-attention
7etos
3D VAE
& other conditions
& other conditions $
Reference
self-attn
3D VAE
DiT
other
Bock
conditions
bone
zeros
(a) Channel Concat
(c) ReferenceNct
(d) Our Reference Injection
Figure 3: Schematic comparison between reference injection alternatives (a)-(c) and ours (d).
Latent Space Projection. All input sequences except the binary masks are encoded into shared latent space using pre-trained 3D VAE. The shape of these latents becomes [batch, frame, channel, height, width] [b. f. h, w]. with their temporal dimension [f] compressed by 4 and spatial dimensions [h. w] compressed by 8. The reference latent remains f 1, and the remaining sequences are scaled to f= (frames 1) // + 1. Next, the first frame the noisy video latent extracted separately to form dummy reference latent, with its subsequent frames zero-padded match the temporal length of the noisy video latent. This latent explicitly represents the first frame of the whole video sequence, enabling the model to focus the extension of the first frame, which provides interface for extrapolating the video length and first frame reference as detailed in Sec. 3.4
Reference Information Injection. We concatenate the reference latent along the temporal dimen- sion [f] with both the noisy and dummy reference latents, and compare with alternatives below:
(i) Channel concatenation: It is straightforward idea concatenate the rero-padded all-copied reference latent along the channel dimension, just as we do with the dummy reference latent and other latents. However, this may disrupt the spatial-temporal feature alignment across frames since the reference latent represents a global signal applicable to all frames, not just frame 0, leading to confused learning and impaired detail injection.
(ii) aotssaattntion: Extracting features such as CLIP embeddings instead of 3D VAE latents for cross-attention leads to limited fine-detail capture due to encoder bottlenecks. Also, cross-attention is better suited for semantic guidance (e.g., text embeddings), not high-fidelity appearance injection.
(iii) ReferenceNet: animation methods, an additional ReferenceNet is typically used to implicitly inject reference features. However, as demonstrated by UniAnimate (Wang et al.] (024a), such operation introduces parameter redundancy and feature space misalignment between reference image and noisy video latent, which can be replaced.
Therefore, we decide to use frame-level concatenation, extending the token length and enabling seamless reference injection. Since the reference is only used for feature extraction and not for prediction, the self-attention mechanism, the video attends to the reference while the reference only attends to itself (using KV cache), shown in the attention mask of Figure 3 The reference latent also excluded from the loss computation, thereby serving as an equivalent implementation ReferenceNet in more concise temporal concatenation manner.
Mask Conditioning and Final Fusion. The binary mask sequence does not require 3D VAE encod- ing. To match the spatial-temporal dimensions of the other condition latents, the masks are processed by concatenating every four frames along the channel dimension, followed by an 8x downsampling 8, 8] shape. Finally, the mask,
in spatial dimensions, resulting in [b, (f agnostic latent, and pose latent are zero-padded on the reference frame, and all the latents are con- catenated along the channel dimension fuse as the final model input.
4 + 1,
In summary, our condition fusion module ensures strict spatial-temporal alignment across all con- ditioning signals. For each frame in the temporal dimension, corresponding conditions of the frame are concatenated along the channel, with additional reference information injected globally via token extension, simplifying the model's learning.
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Fale Saggo
Figure 4: Illustration of adaptive grid sizing across subject scales with extra -hape augmentation.
3.3 ADAPTIVE MASK STRATEGY
As a mask-guided task, the handling of the mask crucial for video subject swapping. Overly precise masks can cause the model to overfit to the mask shape during training, leading to poor generalization (commonly known as shape leakage) when swapping subjects with large domain gaps (e.g. square box spherical ball). Conversely, overly coarse masks may confuse the model's learning, producing artifacts and less refined details. Proper treatment of the mask boundary is therefore essential, governing the subject-context interaction and visual quality of the final result.
Animate Anyone (Hu et al. (225a) addressed such dilemma using a grid-based mask augmentation strategy: the bounding box of the mask is divided into k2 x ki non-overlapping blocks; any block containing mask pixels is dilated, effectively blurring the precise mask boundary to reduce shape leakage. While Animate Anyone 2 is designed specifically for human swapping (animation), our goal is to perform generic subject swapping across diverse scales and attributes. Directly applying their augmentation strategy would result in both small items (e.g. accessories, handheld objects) and relatively large objects (e.g. humans, vehicle, background) sharing the same augmentation, which is suboptimal for both. Thus, we need explore more generalizable mask augmentation strategy.
Adaptive Grid Sizing. To begin with, we randomly use bounding box augmentation with 30% probability, representing the largest shape augmentation (coarsest mask). In the remaining 70% of cases, we inherit the grid-augmentation strategy of Animate Anyone 2, dividing the whole frame rather than the bounding box and making the grid size inversely proportional to the subject scale:
k trein (1,)
K Krrain bbox h/ h//raand(/// h2)
(2)
hinl = h//10
inn bboxhh/hg
Anyone
Adaptive Sizing
where kfrain g.nzf denote the number of vertical blocks into which Animate Anyone 2 divides the mask's bounding box during training and inference; K/rain Kidd caleulate adaptive vertical block number, based on the bounding box height bbox J and hyper-parameters h1, h2, h3s
The horizontal counts k K computed analogously. Intuitively, the larger the subject, the larger bbox J x bbox w, and hence the larger K₁ x Kn. which yields a finer grid, better controlling their movement (and the larger subjects' swapping rarely involves shape leakage). Conversely, small subjects receive coarser masks which accommodate more diverse swapping (c.g., handheld objects other categories). We empirically find that this adaptive strategy improves the generalization for small subjects and enhances the edge and motion control over larger subjects in ablation studies.
Extra Shape Augmentation. After mask augmentation, it be observed that the augmented mask is consistently larger than the target subject. Animate Anyone considers this problematic, arguing that the model may develop a bias in which the generated subject is always smaller than the provided mask; thus, they introduce a random scale augmentation. However, in our view, as long as training and inference follow the same mode-namely, the mask is always slightly larger than the subject-this does not introduce harmful bias. Instead, it simplifies the objective: the model only needs to generate the subject within a mildly expanded region rather than infer a precise mask-subjec correspondence. Therefore, we retain this the default setting.
This design, however, means that the augmented mask inevitably covers some background, requiring the model to perform non- non-subject background completion. Without additional constraints, the model tends to fill the entire mask with reference-derived content, leading to implausible hallucinations, such extending extra hair into the masked region rather than filling reasonable background. To

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mitigate this, we introduce extra-shape augmentation during training. At random intervals, we add simple geometric shapes (circle, triangle, rectangle) to the edges of the rrid-augmented mask. This design further decouples the subject from the mask's exact shape, teaching the model that not all masked pixels belong to the subject. Consequently, the model learns to fill background content more appropriately along the mask boundary, improving subject-context interaction.
Overall, our adaptive mask strategy forms a generie and scalable augmentation scheme that adapts subjects of varying sizes and attributes. Together with the pose and 3D-hand conditions, the grid- based and extra shape augmentations provide richer guidance, producing smoother, more natural boundaries between the swapped subject and its surroundings.
3.4 IMPLEMENTATION DETAILS
We list several technical details that, while not totally novel, proved essential to final performance.
Mask Constraint. Since model is strictly trained in a mask-guided manner. the accuracy of the mask is crucial for final performance. Any regions not covered by the mask will be strictly preserved and serve as context. If the mask contains certain inaccuracies that leak parts of the source subject, the model may draw cues from these regions during swapping, potentially reconstructing the source subject rather than injecting the external reference.
To mitigate this, besides providing a precise mask sequence rather than relying on TrackingSAM.. we recommend a bounding- inference mode, which expands the mask to bounding-box to fully cover the source subject. Moreover, fast-moving or heavily occluded objects may cause TrackingSAM AAM to produce inconsistent masks. At inference, we assume consecutive masks share some spatial overlap; if two adjacent masks overlap S 5%, we use their minimal enclosing region to ensure coverage.
Reference Augmentation. During pre-training, the reference image is always extracted from the same video, maintaining its perfect scale and luminance. This risks the model learning trivial copy- puste behavior (also noted in HunyuanCustom), impairing its ability to handle real-world references with scale, brightness, or angle mismatches. To mitigate this, we apply random scaling, rotation, flipping. and brightness adjustment to the training reference. For severe domain gaps (c.g., inserting half-body human into full-body mask), experiment with random cropping, which reduces the gap but introduces new hallucinations and detail loss. As a trade-off, we disable such cropping.
Two-phase Training Scheme. As mentioned above, the pre-training dataset only uses references extracted from their source videos, limiting generalization to external references with deformations, brightness discrepancies or domain gaps. We address this with two-phase training:
(i) Pre-training (self-attention only): We first train the base model on our Human diD-errvve dataset (Sec. (.2) with only the self-attention layers trainable, preserving the base model's foun- dational generative capabilities. Each reference-video pair shares the same domain in this phase.
(ii) Quality tuning (full fine-tuning): We assemble smaller, high-quality dataset of diverse reference-video pairs by (a) extracting paired images from Anylnsertion (Song et al., (025) and Subject200K (Tan al. 2024), and converting one image of each pair to video via Wan2.1-12V (Wan et al. 2025). and (b) adding 4000 handheld reference-video pairs from AnchorCrafter-400
(Xu et 2024c). All the model layers are unfrozen for full fine-tuning, enabling better conver- gence and adaptation to cross-domain reference-videdd pairs.
Length Extension and First Frame Reference. Our design (Sec. 3.2 dummy reference latent) provides an interface for handling long videos and leveraging first-frame priors.
(i Long video processing: For videos longer than our training length. we first split the video se- quence into overlapping segments: the last frame of the previous segment becomes the first frame and the dummy reference for the next. In subject swapping, the majority of scene content remains constant between segments. This inherent context enables stable temporal extrapolation without the disruptive scene jumps common in general image-to-video tasks.
(ii) First frame reference: Since the dummy reference concentrates the model's attention the first frame, any edit applied there propagates coherently. At inference, users may optionally supply a first-frame swap produced by image subject swapping model like (Chen et al. 2024), enhancing visual fidelity. Notably, DreamSwapV is inherently compatible with single frame swapping, so leveraging specialized image-based models is alternative option for extra stability.
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Tunnel Video Inpainting and Reweighting Loss. When the mask is extremely small (e.g.. cessory), the model may overlook fine details. Inspired by Tunnel Try-on (Xu et al. (024a) and AnchorCrafter (Xu et al. 2024c), we tackle this issue in parallel during both inference and traming:
(i) Inference- tunnell inpainting: At inference time, when the mask area ratio falls below the prede- fined threshold (0.05), we extract tight crop (hwnnel) around the mask, perform subject swapping in this sub-region, and blend this tunnel region back into the full frame, which can focus on pixels where it matters, improving detail fidelity on tiny objects.
(ii) treeeiggnnneeeigtiingg loss: Following AnchorCrrafterXXuerza al. 2024c we enhance the model's aftention to small subject regions by introducing subject-region reweighting loss:
E
Lew
M @ Lpt. Linal = (1-M)) @ Lpt + XLew
(3)
Es
where Lpt, Linall Law are pre-trained loss, overall training objective, and re- weighting loss that am- plifies the learning signal inside the subject mask region, respectively; E and E are the areas of the whole frame and the subject, and M E {0, xx indicates the subject binary mask.
4 EXPERIMENTS
4.1 BASELINES AND BENCHMARK
Basic Information. Our Dwpp is trained on the Wan2.1-12V-14B foundation model, sup- porting up 720p resolution. Our training follows a two-phase scheme: pre-training for 15000 iterations and quality-tuning for 10000 iterations, both on 32 NVIDIA H100 80GB GPUs. Each 100 iterations cost approximately 75 minutes, yielding total training duration of 13 days. Notably,
since we do not modify or depend on the model architecture, our entire framework can be readily transferred other base models, such as subsequent versions of the WanX series (an et al. 2025 or alternatives like Cog VeeeeeeoXYaia et al. 2024) and Hunyuan Video[Kong et al. 2024).
Benchmark. While VBench series (Huang al., 2024 have provided valuable benchmarks for text- to-video and image-to-vin tovideo tasks, similar evaluations for video subject swapping remain absent. To address this gap, we introduce DreamSwap V- Www.waaapeeharr.ttee B-Benchmark, the first benchmark for video subject swapping. Following the scale of benchmarks used in VACE and HunyuanCustom, we collect 100 videos from the online website (Pexels 2025), and annotate 167 distinct subject instances from them. Original videos cover four aspect ratios to test cross-resolution generalization. For each subject, we segment and track its precise mask sequence in the source video, and match it with an appropriate reference image for swapping. The subjects and their reference images for swapping are carefully selected to ensure a wide and balanced distribution across categories and complexities.
We adopt 5 indicators inherited from VBench (see the top metries in Table 1 and design 3 other automatic metrics specifically for the subject swapping task: reference appearance, background preservation and semantic consistency. We also introduce ences for three aspect: reference detail, subject interaction and visual fidelity. The full benchmark distribution, metric calculation rules and user study details are provided in Appendix C.2
study evaluate human prefer-
Baselines. We compare our DreamSwapV against open-source methods (2V VACE, Hun- yuanCustom) and commercial model (Kling 1.6 Multimodal). We select them for their robust stability, highly accessiblity, and close relevance to our subject swapping objective. We exclude text-instruction based methods (VideoGrain, VideoSwap, and VideoPainter) to avoid unfair compar- isons under distinct task settings. See Appendix C.3 for detailed baseline implementations.
4.2 EVALUATION
Quantitative Comparison. Table ! reports the quantitative results on Across the five VBench indicators, our DreamSwapV achieves the best average score, marginally surpassing Kling 1.6. The gap widens when Kling 1.6 suffers from its regeneration-like frame- work, altering large portions of the background even redrawing entire crmpooiin its practical usability. VACE and HunyuanCustom underperform mainly in reference appearance and detail consistency, sometimes failing to insert the correct target subject due to their unified task focus, where subject swapping is only one objective. AnyV2V's unstable intermediate feature manipulations often lead to complete video collapse, severely impairing visual fidelity. Overall, D delivers the most stable and high-quality results, corroborated by the user study.
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Table 1: Comparison video subject swapping methods on CreamSwapV-Bencheee The bold
and underline are the best and second-best results, respectively, and the gray represents our results.
Video Quality & Video Consistency
User Study
Method
Subjecs
Consistency
Background
Condistnncy
Motion
Smaodhness
Dynamic
Degree
Acshhetic
Quality
VBench
Average
Reference
Apparrnes
Background
Preservation
Scmantic
Consistency
Toxal
Average
Reference
Detail
Subjece
Interaction
Visnal
Fidelity
/ Metries
V2V
90.03%
91.35%
98. 600%
47.90%
51.79%
75.93%
34.70%
42.71%
51.00%
63.51%
0.87
0.65
0.42
VACE
96.15%
95.03%
99.29%
27.54%
56.95%
74.99%
39.66%
47.46%
66.93%
66. 16%
2.31
2.46
HunyuanCustom
95.83%
94 94.96%
99.17%
43.11%
57.78%
78.17%
41.33%
48. 14%
63.65%
68.00%
2.17
2.22
2.13
Kling
96.57%
99.45%
57.26%
DreamSwap
94.26%
99.31%
55.69%
56.52%
45.22%
52.49%
72.01 %
71.49%
3.32
Half-body Human
Garment
Handheld
HuyyarCysoo
VACE
AnyVEV
Full Full-hody hody Human
Vchicle
Background
Kling
HunyyanCCattem
AsyVEV
Figure 5: Qualitative comparisons between our DreamSwapV and other baselines on various sub- jects and video aspect ratios. Please zoom in for details, and refer supplementary dynamic videos.
Qualitative Results. Figure 5 provides a visual comparison highlighting DreamSwapV's advan- tages across 6 different kinds of subjects (see Appendix D for more results). In the sumaan=Lufh case, DreamSwap's successfully swaps the person with Luffy, preserving his original pose and refer- ence details, while Kling struggles with reference consistency, HunyuanCustom and VACE inject an

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Published as a conference paper at ICLR 2026
Table Quantitative comparison of different ablation settings (w/o reference injection, w/o adaptive grid sizing, w/o extra shape augmentation or w/o two-phase training) with our full version model. The bold are the best results under certain metries, and gray marks the full version model.
Video Quality & Video Consistency
Ablation
Subject Consistency
Background
Consistency
Mation
Smoothness
Dynanic
Degree
Aeshetic
Quality
VBench
Avenge
Reference
Appearanes
Background Preservation
Semantic
Consistency
Total
Average
Metries
w/o our reference injection
channel conca!
93.87%
93.24%
99.11%
48.60%
54.17%
77.80%
37.34%
51.57%
58.22%
67.15%
cross-attention
95.49%
94.21%
99.29%
52.67%
56.41%
79.61%
39.23%
51.84%
62.93%
69.11%
w/o our adaptive grid sizing
96.07%
94.244%
99.31%
54.37%
56.39%
80.08%
39.29%
52.70%
65.55%
69.81%
w/o our extra mask
96. 12%
94.11%
99.30%
55.66%
55.69%
80.19%
43.611
52.12%
66.03%
70.34%
w/o two-phuse training
96.38%
94.04%
99.31%
55.67%
55.37%
80.15%
43.52%
52.41%
65.14%
70,239
Full (ours)
96.41 %
94.26%
99.31%
55.69%
56.52%
80.44%
45.22%
52.49%
72.01
71.49%
reference
mask
source video
w/o our reference injection const-attvaaioon
w/ reference injection
chonne crociat
reference
mask
w/o our adaptive mask sizing
w/ our adaptive mask sizing
Figure 6: Ablation examples our reference injection and adaptive grid sizing, zoom in for details.
almost static subject, and Any collapses completely. In the Coat=Coat and Pen=+ case, all baselines fail to maintain the reference appearance, whereas DreamSwapV retains fine details and realistic motion, yielding the highest visual quality.
More applications. is worth noting that DreamSwapV is not limited to video subject swapping, but can extend to several related applications like image swapping, video inpainting, video addition, video try-on and so on. We further discuss these extensions in Appendix
Limitations and failure modes. As discussed in Scction 3.4 certain cross-domain swapping modes (e.g., inserting a half half-body human into a full-body mask) pose challenges for our model. For cases such animal - human object character, the poses of animals or objects differ significantly from humans, further increasing the difficulty for our pose-conditioned approach. We leave the task of cross-domain swapping to future mask-free work. For more limitations and our future plan, please refer to Appendix E.4
4.3 ABLATION STUDY
We ablate on our reference injection of condition fusion module and adaptive grid sizing with visual examples in Figure 6 Our reference injection achieves finest details over alternatives like channel concatenation and cross-attention. Our adaptive grid sizing can better handle subjects with different scales and improve visual fidelity. We also conduct quantitative ablation study reference injection, adaptive grid sizing, extra shape augmentation and two-phase training in Table 2
CONCLUSION
In this paper, we present DreamSwapV, a mask-guided and subject-agnostic framework for end-to- end, generic subject swapping in video customization. We introduce a dedicated condition fusion module and an adaptive mask strategy to integrate multiple conditioning signals efficiently, enabling fine-gra line-grained control and more natural subject-context interactions. Our elaborate two-phase dataset construction and training scheme further enhance DreamSwap W'''''s capabilities, enabling it to outper- form all existing methods VBench indicators and first introduced DreamSwap V-Bnechhark.
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ACKNOWLEDGEMENTS
This work is supported by the NSFC fund (62576190), in part by the Shenzhen Science and Tech- nology Project under Grant 1032100000001000000 10100000010111000000000000000000
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A OVERVIEW
This technical appendix includes additional details and information about our methods and exper- iments which cannot be fully covered in the main paper limited by space. We first strongly rec- ommend readers to watch the video demo (Appendix B provided along with this appendix in the supplementary material, where we dynamically showcase our entire pipeline and present the video comparison results. Then we complement our full implementation details for reproducibility, in- cluding parer-parameers, benchmark contents, and baseline adaptations (Appendix C). We further provide additional experimental results which camnot be fully displayed in the main vaper(Appendle D). At last, we provide comprehensive discussion, including the more applications, social impact, large language model (LLM) usage, limitations and future work (Appendix
B
VIDEO DEMO
Our video demo is provided the supplementary material and consists of:
The brief presentation summarizing the main paper.
Dynamic pipeline and data flow, which brings clearer and more complete understanding of our proposed method.
Video comparisons of our method and four baselines across diverse subject categories and aspect ratios.
More application examples demonstrating the strong extensibility of our method.
It is worth noting that, due to the 100 MB file size limit, the video demo has been compressed and may appear slightly degraded quality. The original results are clearer and smoother; however, since all videos are compressed to the same degree, the relative performance among methods remains unaffected.
C IMPLEMENTATION DETAILS FOR REPRODUCIBILITY
C.1 SPECIFIC
The specific spper-parameters DreamSwapV our two-phase training scheme are listed in Table 3 for better reproducibility.
C.2 DREAMSWAPV-BENCHIEE CONTENTS
Benchmark Distribution. The subjects and their reference images for swapping from DreamSwapV-Benchne B-Bnechharrk are carefully selected to ensure wide and balanced distribution across categories and complexities.
Figure 7 is the 2-D histogram density of all reference images after t-SNE (Maten Hinton 2008
projection: each colour block represents the number of images falling into 8x8 grid map on the embedding plane. The cell-count coefficient of variation 0.736 and the x" goodness-of-ff test gives p 0.952 (> 0.05), showing that only a very small fraction of grid cells are empty over- populated-i.e. the images cover the feature space almost uniformly.
Figure 7b is the t-SNE scatter plot coloured by K-Means clusters (k = 12) produced in the original 2048-D feature space. The clusters have a size CV of 0.495 and a normalised Shannon entropy of 0.947 (~ 1), meaning the images evenly split across latent semantic groups with no dominant or under-represented cluster.
Together, the two figures verify that our benchmark set is both spatially well-covered and semanti- cally balanced, preventing bias in subsequent experiments.
Metric Calculation. We adopt five indicators inherited from VBench (Huang et al. 2024 sub- ject consistency, background consistency, dynamic degree, motion smoothness, and aesthetic qual- ity iny-and and design other automatic metrics specifically for the subject swapping task: reference
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Table 3: Hyper-parameters in our two-phase training.
Hyper-parameters
Pre-training
Iterations
15000
Learning Rate
2e-5
LR Scheduler
constant.wi th.
Batch size
Optimizer
AdamW
Resolution
1280x720
Frame Length
65
CFG Drop Ratio
0.15
Image Mix Ratio
0.3
Flow Shift h1, 12, h3
5.0
3,
Quality Tuning
Iterations
10000
Learning Rate
le-5
LR Scheduler
constant.with.waare
Batch size
Optimizer
AdamW
Resolution
1280x720, 720x 1280, 512x512, 1024x 1024
Frame Length
69
CFG Drop Ratio
0.15
Image Mix Ratio
0.1
Flow Shift
5.0
h1, f22 ha
1,30,
2-0 Histagram Density
L-SNE Scatter KiNaaaa Clister
2.5
03
EV = 0-228
X 6.600
Erissy 23N7
(a) 2-D histogram density of references (t-SNE plane).
(b) t-SNE scatter coloured by K-Means (k = 12).
Figure 7: DreamSwapV- distribution analysis. (a) shows spatial coverage and (b) shows semantic balance across clusters.
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appearance and background preservation. The specific meanings and calculations of each metric listed below:
(i) Subject consistency: The indicator measures temporal subject-identity consistency- -ic. how stable the visual appearance of the main subject remains from frame to frame within single video. Let d2 RD denote the fz-normalised DINO (Caron et al. 2021) feature of frame t in T-frame video. Temporal subject identity stability is quantified by
T
(d1, di) + (d- (d)-1.
Suujecct
T
2
1=
where (.-)) is the cosine similarity. The score is averaged over all test videos of a model; larger values indicate greater subject consistency.
(ii) Background consistency: This indicator measures the temporal consistency of the background identity throughout video. For each of the T frames, CLIP image encoder (Radford et al.) 2021) yields unit-normalised feature vector Temporal background consistency is then
(e1, ct) + (c--1.
Stackground
T-1
2
1=2
where ( denotes cosine similarity. The model-level score is obtained by averaging Shackgmund across all evaluation videos; larger values indicate more consistent background.
(iii) Dynamic degree: This dimension gauges the tendency of model to produce non-static videos. For each generated clip with frames, RAFT (Teed & Deng 2020) employed to estimate dense optical flow between consecutive frames, yielding magnitudes {m,,;) Let A mean (top-5%{ my,t }) denote the average of the largest 5% flow magnitudes, which is sensitive to small-object motion. A clip is classified as dynamic if X, > where is fixed empirical threshold. The model's dynamic degree is the proportion of dynamic clips:
N
Sdynamic
Σ
1/x
> T).
N
with N the number of evaluation videos and 1.: the indicator function. Higher values imply a stronger propensity generate motion.
Smooth, physically plausible motion is assessed via a frame-interpolation prior. Given (2n+1)-framd clip ? every second frame is removed to form the fow-frame-
(iv) Motion smoothness:
rate sequence {f2x 1a o a pretrained interpolator then predicts the missing frames [fex+] 1 } 1n-1 Temporal smoothness is quantified by the complement of the normalised mean absolute error (MAF) between the predicted and original odd frames:
1f2411 fen-
Smation =
C
k=
where is the normalisation constant (as in Eq. 4) that bounds Smotion E [0, 1]. Averaging Smetion across all test videos yields the model-level score; larger values indicate smoother motion.
(v) Aesthetic quality: Photographic composition, colour harmony, and general artistic appeal are assessed with the LAION image- maag-aesthettis predictor (LAION-AI 2022). which returns per- frame score a, E [0, 10] for each of the T frames. Scores are linearly mapped to [0, 1] and averaged:
1
Saesthetic
T
10
Averaging Saeshetic across all clips gives the model-level score; larger values indicate higher aesthetic quality.
(vi) Reference appearance: While the VBench subject consistency metric verifies that the swapped subject remains visually stable across video frames, it does focus how faithfully that subject
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mirrors the external reference image provided by the user. We therefore introduce the reference appearance metric to measure appearance fidelity. The same DINO backbone used subject con- sistency is employed, but the caleulation is changed: each video frame is compared directly to the reference image rather than to other frames.
Let de be the fz-normalised DINO feature of frame t in T-frame clip and d, the feature of the reference image. The metric is the mean cosine similarity
Steference
Σ (dr, de),
which lies in [0, 1]. Higher values indicate that the swapped subject in the video retains the color, texture, and overall appearance of the reference image more closely. By complementing subject- consistency, this metric allows a comprehensive assessment of both temporal stability and appear- ance fidelity in video subject-swapping tasks.
(vii) Background preservation: The VBench background metric measures intra-video background identity but cannot reveal how much the scene diverges from the original footage after video subject swapping. To capture that difference, we supply with the background preservation metric directly comparing each swapped frame to source counterpart outside the tracked object mask.
For every time step I E {I...., T). U. and Vn be the original and swapped frames, and Mi denote the background pixels (the complement of the user-provided mask). The peak signal-to- noise ratio on the background region is P. = PSNR(U, @ MA, V/2 @ M;) (measured in dB). A monotone rescaling maps PSNR to (0, 1) and is averaged across the video frames:
S preservation
1
Σ 1=1
T
P./50
T
P./50
+ 0.6
Higher values indicate that the background is better preserved after swapping the subject, whereas lower scores signal unintended scene alterations.
(vii)) Semantic consistency: Subject swapping may change the high-level semantics of the target subject. To assess whether the generated video preserves the semantic meaning specified by the reference, we compute a caption-based semantic similarity using BI.IP-2(Li et al.) (023). a recent vision-language model capable of producing robust semantic embeddings.
For each time step t E (1...., T), BLIP-2 generates a caption embedding from the masked subject region in frame and c, denotes the caption embedding extracted from the reference image. All embeddings are fz-normalized. The semantic consistency score is defined as the mean cosine similarity:
1 T
Sxemantic
Σ T [=1
+ exp(-kss.)
where h: 50 is fixed parameter that maps the small raw similarities into stable (0, 1) range while preserving their ordering.
This metric reflects whether the video maintains the category, coarse shape, and other high-level semantics of the reference subject, thereby complementing appearance- and background-baad eval- uations.
User Study Details. To further examine human preferences of our method and four baselines, we conduct user study from three aspects:
Reference detail instructs annotators to judge how faithfully the fine-grained details of the reference image retained after subject swapping, paralleling the automatic metric refer- ence appearance.
Subject interaction focuses on the interaction between the inserted subject and its surround- ings, assessing both the visual plausibility of that interaction and whether it remains strictly confined to the user-specified region.
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Video 2/ 167
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Rank each method (1 = best)
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Figure 8: The streamlit annotation interface of user study.
Visual fidelity gauges the overall perceptual quality of the video how realistic it appears and whether any synthetic or Al al-generated artifacts are discernible.
15 annotators are carefully selected: five specialists in video generation or editing, five practitioners or students from other areas of artificial intelligence (AI), and five individuals with no Al back- ground. The streamlit (Khorasani et al.) 2022) annotation interface (Figure 8) presents each rater with five videos at time. For each metric, the annotators are required to produce strict ranking of the five videos from first to fifth, with no ties permitted.
The rankings are then converted into numerical scores using a weighted Borda-count (Emerson, 2013) scheme: 1st = 5 points, 2nd 3 points, 3rd 2 points, 4th point, and 5th 0 points. Scores are aggregated per metric and per method, and finally averaged over the 15 annotators to yield the overall user-study results for each method.
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C.3 BASELINE ADAPTATIONS
2025). HunyuanCuston (Hu jet al., 2025b) and Kling 1.6 Multimodal (Keling 2025) for their robust stability, high accessiblity, and close relevance our subject swapping objective.
We select four baselines: Vny ell 2024). VACE (Jiang et a
Specifically, for Any we edit the first frame as required using the latest image subject swap- ping method InsertAnything (Song et al. 2025), and utilize V2y'sV's default 12VGen-XL pipeline to obtain subsequent edited frames. For VACE and HunyuanCustom, we preprocess the mask se- quences as specified by each method to ensure their best performance. For Kling 1.6 Multimodal, we use its official in browser masking tool to define the source subject. Notably, this tool occasion- ally struggle with specific selection (e-g. garment inside the human region), limiting the number of valid annotations. We process as many instances from our benchmark as possible, yielding a total of 152 Kling results. Since these four methods all support text inputs but not solely rely on them, we provide the text prompts by briefly describing of the subject-reference pair.
PikaSwaps PPik 2025) also commercial model specializes in video subject swapping, but its API is consistently unavailable due to high demand during our whole evaluation period. If the API returns normal operation, we will incorporate its results into benchmark the earliest opportunity.
D ADDITIONAL EXPERIMENTS AND RESULTS
present extensive visual comparisons between DreamSwapV and the four baselines, spanning subject categories from human and garment to small and large object. Across all cases, our method delivers superior visual fidelity and stability, corroborating the state-of-the-are art performance reported in the main paper.
Figures I0 12
E
FURTHER DISCUSSION
E.1 MORE APPLICATIONS
As noted in the main paper, our method can be extended to numerous related or downstream tasks, which we will analyze individually below.
Image Subject Swapping. Our framework is inherently image-compatible: a single image is treated as frame=l video. Figure a confirms that this simple adaptation yields high-fidelity image subject swapping.
Video Inpainting or Addition. As discussed in Section 3.1 slight changes in the input modality transform video subject swapping into video inpainting or video addition:
Impainting. If the reference image is omitted, the model automatically hallucinates plausi- ble content inside the mask, restoring the damaged region (Figurepp)
Addition. Conversely, supplying an external mask sequence that covers empty space prompts the model to insert the reference object, effectively adding a moving subject to the clip (FigurePee.
Video Try-on. When the target subject is constrained to garments, DreamSwapV acts as video try-on system, as already illustrated in Figure For more challenging scenarios like layered outfits and complex wrinkles, our model can serve stage on downstream try-on dataset, which we leave as future work.
strong prior and adapt after brief quality tuning
Hand-Object Interaction. Limiting the subject to hand-held items turns the task into Land-object interaction (HOI) editing (Figure IT]. Unlike template-based eeference-to-video(RRe such as AnchorCrafter (Xu et al. 2024c) or DreamActor-HI (Wang et al.) 2025). our approach directly swaps the product inside the original video an avenue that remains unexplored. Same extension to video try-on, with short, task-specific quality tuning, DreamSwapV could further bridge this gap and advance HOI video editing, a promising direction we also earmark for future exploration.
pipelines
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(a)
source image
mask
references DreamSwapV image subject swapping
(b)
source video
mask
agnostic
DreamSwapV video inpainting
(c)
source vidco
reference
mask
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Figure 9: More applications of our DreamSwapV on (a) image subject swapping, (b) video inpaint- ing, and (c) video addition, demonstrating the strong extensibility and transferability of our model to related and downstream tasks.
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E.2 SOCIAL IMPACT AND ETHICS STATEMENT
Positive Impacts. DreamSwapV broadens the reach of high-quality video subject swapping and related editing tasks by:
Lowering production barriers. Content creators, educators and small studios can replace actors, props or garments without reshooting or green-screen setups, making professional video customization affordable and time-efficient.
Enabling new commercial and creative workflows. The same pipeline supports video try-on, product insertion and damage inpainting, giving advertisers and e-commerce plat- forms rapid, personalized marketing assets while helping archivists restore legacy footage.
Research reusability. Although DreamSwapV is initially fine-tuned from the Wan2.1-12V 14B backbone (Wan et al. 2025). remains model-inde inde--independent: the framework is fully plug-and-play. allowing researchers to attach it to any DiT-based I2V model and potentially achieve even stronger
performance.
Risks and Challenges. The same ease of use facilitate malicious deepfakes, brand counterfeits, or non-consensual impersonations; attackers need only a reference photo to graft someone into misleading footage. Copyright conflicts may arise if trademarked characters or logos are swapped into commercial videos, and highly convincing forgeries could undermine public trust in authentic media.
Ethical Concerns. Responsible use requires securing permission from anyone depicted in refer- ence images and respecting intellectual- property rights for all source material. Clear disclosure of edited content, along with basic provenance tools (e.g., watermarking or edit logs), helps viewers distinguish synthetic footage from genuine video.
E.3 LARGE LANGUAGE MODEL USAGE
We OpenAl's GPT-4o model (OpenAl 2025) language assistant improve the clarity and consistency of this manuscript. Its primary roles included:
Language Polishing:
Refining grammar, terminology, and sentence flow to better meet academic writing standards.
Concise Rephrasing:
Shortening verbose descriptions while preserving technical accuracy.
KTЕX Formatting: Improving equation and table formatting in ETEX for readability.
All outputs are reviewed and revised by the authors to ensure technical correctness. The model contributes new scientific ideas or experimental results-its role is strictly limited to writing support.
E.4 LIMITATIONS AND FUTURE WORK
Although fruitful, need to admit that DreamSwapV still has several limitations. First, a single reference image conveys only limited information about the target subject; when the subject rotates its backside is revealed, the swapping quality may degrade. Incorporating multi-view reference inputs akin to AnchorCrafter(XX et al. 2024c)- is potential solution. Second, since rigid objects lack an explicit pose, fine-gran men-grained control remains challenging; introducing spatial coordinate sys- tem (as in Orient Anything WWng et al.) (024b)) to serve as the object's s pose representation may yield more precise swapping. Third, the substantial training and inference costs of our full model may restrict its practicality for smaller-scale applications; version in the future for broader use. Finally, as discussed in Appendix [E.] adapting DreamSwapV to downstream tasks such as video try-on and hand-object interaction represents promising search direction. In summary, while our current model has limitations, these potential enhance- ments open new pathways for future development. We eagerly anticipate the next explorations of PreamSwapV-let'ss stay tuned!
therefore plan to release distilled
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DronShap
Kling
HurouanCuttoe
WALE
Azy/2V
DestnSwapy
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VACE
AXy
Figure 10: More qualitative comparisons between our DreamSwapV and four baselines in the human category (full body, half body and talking head), Please zoom for details, and see the video demo for dynamic presentations.
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DrenS
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NO JUSTICE NO PI PRACE
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DreanSwapl
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Figure II: More qualitative comparisons between our DreamSwapV and four baselines the small object category (handheld object and others). Please zoom in for details, and see the video demo for dynamic presentations.
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King
VACE
DreasSiospp'
King 1.6
VALT
AnyoV
Figure 12: More qualitative comparisons between our DreamSwapV and four baselines in the gar- and large object categories (vehicle, statue and sky). Please zoom for details, and the video demo for dynamic presentations.
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Reviews:
[{"review_id": "Dxsu9kIEHr", "reviewer": "Reviewer_pJC7", "summary": "This paper proposed DreamSwapV, an mask-guided framework for video subject swapping that allows users to swap any subject in any video using a mask and reference image. It introduced a multi-condition fusion module and an adaptive mask strategy to handle subjects of varying scales and attributes. DreamSwapV leverages an elaborate two-phase training scheme on a carefully curated dataset and introduced the DreamSwapV-Benchmark, and achieving best performance on the benchmark.", "questions": "Q1) Since phase 1 is mainly trained on human (HumanVID), and phase 2 is trained on small subjects (Subject200K etc), this raise an interesting question. How much gain did the model get after each stage? Comparing the model’s performance after each training phase would give more insights on how effective this method is.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "S1) The method is well ablated and justified the design choices that yields finer details and more robust subject-context integration.\n\nS2) Adaptive mask makes a good design by dynamically adjusting the grid size based on the subject's scale and augmenting mask boundaries with geometric shapes.\n\nS3) Its great to see the discussion on handling long videos, as most related works can only edit videos on a certain length / training length.", "weaknesses": "W1) In Table 1, the automatic metrics \"Video Quality & Video Consistency\" show AnyV2V with high scores on most quantitative indicators (subject consistency, background consistency, motion smoothness), nearly on par with other leading methods. However, the user study metric (human-rated reference detail, subject interaction, and visual fidelity) shows AnyV2V achieving nearly zero. This shows the proposed metrics are insufficiently sensitive to qualitative breakdowns. It seems only the reference appearance and background preservation metrics are making sense here. The authors might consider replacing VBench metrics with alternatives.\n\nW2) Since the method is guided on the masks, the quality of the mask directly affects the final swapping results. Although detection models like TrackingSAM can handle the mask, but any errors could happen in fast motions and propagate to the downstream swapping result. Any remedy to make sure the mask is correctly handled?", "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": "vPGjQ0VJUG", "reviewer": "Reviewer_5Z3c", "summary": "The paper presents DreamSwapV, a mask-guided, subject-agnostic framework for end-to-end video subject swapping. The method reformulates the swapping process as a video inpainting task, enabling more seamless integration between the inserted subject and the background. The proposed system combines a condition fusion module that integrates multiple signals and an adaptive mask strategy that accommodates subjects of varying scales.", "questions": "My key questions are related to the weaknesses mentioned above; besides those, I still have a few minor questions.\n* How does the model perform on truly cross-domain swapping (e.g., animal → human or object → character), and what failure modes are observed?\n* How does the method behave under mask noise or misalignment, such as slightly inaccurate user-provided masks — does performance degrade sharply or remain stable?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* The combination of a multi-condition fusion module and adaptive mask strategy is intuitive yet effective, addressing common artifacts, e.g., boundary leakage and poor subject-context blending.\n\n* The proposed DreamSwapV-Benchmark is a valuable addition to the field, with well-defined metrics and an attempt at quantitative evaluation for a task lacking standard benchmarks.\n\n* The paper is clearly written and easy to follow, with well-organized figures and methodological explanations.", "weaknesses": "* While the new benchmark is appreciated, the evaluation relies heavily on VBench-style metrics that may not capture identity preservation or semantic consistency robustly. A few qualitative examples are shown, but it remains unclear how the model generalizes to truly out-of-domain subjects or complex dynamic interactions.\n* Some ablations, e.g., condition fusion variants, mask augmentation parameters, are only qualitatively discussed. Quantitative ablations would make the argument stronger.\n* The method is heavily trained and evaluated on HumanVID-derived data, raising concerns about its generalization to more diverse subjects.", "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": "0xGeKLMePH", "reviewer": "Reviewer_Wef7", "summary": "This paper presents DreamSwapV, which is a mask-guided and subject-agnostic framework for video subject swapping. It allows users to replace any subject in any video using a mask and a reference image. The method treats swapping as a video inpainting task, enabling seamless blending between the new subject and the original scene. A condition fusion module integrates mask, pose, and 3D-hand inputs, while an adaptive mask strategy handles subjects of different sizes. The model is trained in two phases for better generalization. Experiments on a new DreamSwapV-Benchmark show that it achieves higher visual quality and consistency than existing methods.", "questions": "1. Can you provide some failure case analysis for your method?\n2. The two-phase training scheme and dataset mixture seem central to performance. Could the authors clarify how much improvement the second phase brings quantitatively, and whether similar performance could be achieved with a single large-scale fine-tuning?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposal of the condition fusion module and adaptive mask strategy enables fine-grained control, resulting better subject-context interaction together with high-quality visual improvements.\n2. The paper introduces a new benchmark DreamSwapV-Benchmark in customized image editing domain and the proposed method shows improvements over previous baselines with both quantitative metrics and user studies.\n3. The model can be extended beyond subject swapping to related tasks like video inpainting, addition, and try-on, which may show good generalization potential.", "weaknesses": "1. The method relies on many detailed design choices and a two-phase training process across multiple datasets. While these improve performance, they make the system complicated and harder to reproduce.\n2. Some baselines, like AnyV2V, are training-free or designed for broader editing rather than subject swapping, which makes the comparison less fair.", "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": "4zUnQWnqFR", "reviewer": "Reviewer_bJk4", "summary": "This paper proposes an end-to-end framework based on mask guidance, which enables the replacement of any subject in a video using a specified mask and a reference object. Its key contributions are as follows:\n\n- The proposal of an adaptive masking strategy to address practical issues.\n- A dedicated conditional fusion module that integrates rich conditional information.\n- The introduction of a new benchmark.", "questions": "1. In the Extra Shape Augmentation section, this paper mentions intentionally making the mask slightly larger than the target subject, which the authors consider a better approach. However, this approach is not supported by quantitative evidence in the subsequent experiments, and additional relevant experiments are expected to be supplemented.\n2. This paper employs the newly proposed Benchmark for model evaluation in the experimental section. The evaluation results seem to be inconsistent with the quantitative results of Hunyuan Custom. Traditional quantitative comparison methods remain indispensable, and supplementary experiments in this regard are anticipated.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper has a clear logical flow, elaborating on its key contributions with a focus on two main aspects.\n2. The adaptive masking strategy takes full account of various practical issues and achieves favorable results.\n3. This paper demonstrates substantial research effort, solid argumentation, and has obtained excellent model performance.", "weaknesses": "1. There is still room for improvement and supplementation in the quantitative results of the experimental section of this paper.\n2. The work of this paper mainly focuses on the targeted processing of data, with limited improvements to the model itself.", "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)