EasyCreator: Empowering 4D Creation through Video Inpainting
Authors: Yue Ma
Kunyu Feng
Xinhua Zhang
Hongyu Liu
David Junhao Zhang
Jinbo Xing
Yinhan Zhang
Xiangpeng Yang
Xinyu Wang
Zeyu Wang
Qifeng Chen
Venue: ICLR 2026 Poster (2026)
Keywords: diffusion model; 4D video generation and editing
Primary area: generative models
TL;DR: We reformulate 4D video creation as a video inpainting task and achieving the 4D video creation with camera trajectories and edited first frames

Abstract: We introduce EasyCreator, a novel 4D video creation framework capable of both generating and editing 4D content from a single monocular video input. By leveraging a powerful video inpainting foundation model as a generative prior, we reformulate 4D video creation as a video inpainting task, enabling the model to fill in missing content caused by camera trajectory changes or user edits. To facilitate this, we generate composite masked inpainting video data to effectively fine-tune the model for 4D video generation. Given an input video and its associated camera trajectory, we first perform depth-based point cloud rendering to obtain invisibility masks that indicate the regions that should be completed. Simultaneously, editing masks are introduced to specify user-defined modifications, and these are combined with the invisibility masks to create a composite masks dataset. During training, we randomly sample different types of masks to construct diverse and challenging inpainting scenarios, enhancing the model’s generalization and robustness in various 4D editing and generation tasks. To handle temporal consistency under large camera motion, we design a self-iterative tuning strategy that gradually increases the viewing angles during training, where the model is used to generate the next-stage training data after each fine-tuning iteration. Moreover, we introduce a temporal packaging module during inference to enhance generation quality. Our method effectively leverages the prior knowledge of the base model without degrading its original performance, enabling the generation of 4D videos with consistent multi-view coherence. In addition, our approach supports prompt-based content editing, demonstrating strong flexibility and significantly outperforming state-of-the-art methods in both quality and versatility.

Published as a conference paper at ICLR 2026
EASY EMPOWERING 4D CREATION THROUGH VIDEO INPAINTING
Yue Ma Kunyu Feng 21 Xinhua Zhang 41 Hongyu Liu 1x David Junhao Zhang
Jinbo Xing Yinhan Zhang 2, Xiangpeng Yang. Xinyu Wang " Zeyu Wang 2 Qifeng Chen 12s HKUST, HKUST(GZZ, 3 NUS, Tsinghua University, CUHK
Output Imput
A Bulbasaur is watering the flowers.
a cat wears gold hoop in the snow.
robot dog giant man Japanese room.
View
www.
Input
giant man a Japanese room with sunflower painting.
Figure 1: Showcases of our EasyCreator We reformulate 4D video creation as video inpainting task. Given an input video, FasyCreatorenables 4D video creation with various camera trajec- tories (bottom left the input video's first frame) and edited first frame (bottom right), while maintaining multi-view consistency. In addition, it supports flexible prompt-based content editing (e.g., adding robot dog sunflower painting).
ABSTRACT
We introduce FasyCreator, novel 4D video creation framework capable of both generating and editing 4D content from single monocular video input. By lever- aging a powerful video inpainting foundation model as a generative prior, we reformulate 4D video creation video inpainting task, enabling the model to fill in missing content caused by camera trajectory changes or user edits. To facilitate this, we generate composite masked inpainting video data to effectively fine-tune the model for 4D video generation. Given an input video and associated camera trajectory, first perform depth-based point cloud rendering obtain invisibility masks that indicate the regions that should be completed. Simultaneously. editing masks introduced to specify user-defined modifications, and these combined with the invisibility masks to create a composite masks dataset. During training, we randomly sample different types of masks to construct diverse and challenging in- painting scenarios, enhancing the model's generalization and robustness in various 4D editing and generation tasks. To handle temporal consistency under large cam- era motion, we design self-iterative tuning strategy that gradually increases the viewing angles during training, where the model used generate the next-stage training data after each fine-tuning iteration. Moreover, we introduce temporal packaging module during inference to enhance generation quality. Our method effectively leverages the prior knowledge of the base model without degrading its
Equal contribution
Project Leader
Corresponding author

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Published as a conference paper at ICLR 2026
original performance, enabling the generation of 4D videos with consistent multi- view coherence. In addition, our approach supports prompt- content editing, demonstrating strong flexibility and significantly outperforming state-of-the-ar methods in both quality and versatility.
INTRODUCTION
Video generation foundation models (kel, 2024; Kong et al., 2024; Wang et al., 2025) have attracted considerable attention and witnessed rapid progress in recent years. These models are capable of synthesizing high-fidelity, temporally coherent videos from coarse user inputs such as text or image prompts. As the capabilities of such models continue to evolve, there is increasing demand for more precise and controllable video generation (He et al., 2024; Ling et al., 2024; Wang et al., 2024a; Xing et al., 2025), where additional modalities, such as audio (Xing et al., 2024c), human pose (Hu et al., 2023; Ma et al., 2024a; Peng et al., 2024), or depth (Gen, 2023; Guo et al., 2024; Pang et al., 2024; Xing et al.. 2024b), are leveraged to guide content creation. These advancements aim to better align the generated outputs with users' creative intent and enhance the expressiveness and relevance of the synthesized videos.
4D video generation is an emerging paradigm of controllable video synthesis that enables dynamic content creation guided by camera trajectories. It has garnered increasing attention for its ability to produce cinematic effects such as camera motion and bullet time, supporting immersive and expressive visual storytelling. Recent approaches (Bahmani al., 2024; Bai et al., 2024) typically incorporate camera trajectories into pre-trained video generation foundation models by encoding them as embeddings, analogous to text prompts. These methods often rely on multi-view datasets, synthetic renderings, monocular videos with annotated camera poses for model fine-tuning However, despite recent progress, several limitations remain, including a strong dependence on large-scale training data, restricted input modalities, commonly limited to images or text, and limited controllability over camera viewpoints. Notably, current methods lack support for video inputs and are unable to transform user-provided monocular videos into coherent 4D representations.
To achieve more realistic generation results and enable the conversion of monocular videos into 4D representations, recent methods (Jeong et al., 2025; Ren et al., 2025; YU al., 2025; Zhang et al., 2024a) often decompose the task into two stages. The first stage employs existing depth predictors (Hu et al., 2025) to estimate per-frame depth from monocular videos, generating dynamic point clouds that are rendered along desired camera trajectories. This rendering process typically results in videos with masked regions (holes) caused by occlusions or incomplete geometry. In the second stage, video inpainting is applied to fill these holes and produce the final output. While dynamic point clouds can now be reliably obtained using well-developed depth estimation techniques, appropriate video inpainting models for this task remain underdeveloped. Consequently, current approaches often rely on collecting additional data to fine-tune general-purpor image or text-to-video foundation models for inpainting. However, since these foundation models are not originally designed for video inpainting, they frequently fail to produce temporally consistent and visually realistic completions. Moreover, such fine-tuned models typically do not support text-based editing during generation, which significantly limits their flexibility and practicality for 4D) video editing.
Fortunately, powerful video inpainting foundation model, Wan2.1 (Wang et al., 2025) has recently emerged, trained on large-scale datasets. However, we observe that it cannot be directly applied to complete the masks (i.e., occluded regions) introduced by point cloud rendering, as such masks fall outside its training distribution. In this paper, we propose EasyCreator, a novel 4D video generation framework that reformulates 4D generation as a specialized video inpainting task. Our goal is to unlock the potential of powerful video inpainting models for 4D reconstruction, enabling realistic, flexible, and controllable results with minimal additional training. Specifically, we first utilize an off-the-shelf depth predictor (Hu et al., 2025) to estimate per-frame depth maps, which are then transformed and aggregated into dynamic point clouds. These point clouds are rendered using a double-reprojection strategy (YU et al., 2025) to generate sequence of masks from the target camera viewpoint, projected back to the original camera poses. These masks correspond to occluded or invisible regions caused by rendering and serve as the completion targets during 4D generation. In addition, we construct an editing mask sequence to define regions requiring content modification. The occlusion and editing masks can be used individually combined, forming a composite mask
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dataset with three types of masks. During fine-tuning, we randomly sample one type of mask for each training instance, allowing the foundation model to be sufficiently trained while supporting both 4D generation and editing. Furthermore, increases viewpoint diversity by reusing results from previously trained views as training data for subsequent ones, improving stability under large camera motions.
introduce self-iterative tuning strategy that progressively
To ensure multi-view consistency during inference, we present a temporal packing strategy that en- hances coherence across frames and viewpoints by leveraging previously generated results as priors to guide content completion within masked regions. We conduct comprehensive evaluations of EasyCre- ator on both synchronized multi-view datasets and large-scale monocular video datasets. Quantitative results and qualitative visualizations consistently demonstrate that our method outperforms existing approaches in generating high-fidelity videos under novel camera trajectories.
2 RELATED WORK
Camera-controlled video generation. Following the success of text-to- vide generation models (Bar- Tal al., 2024; Chen et al., 2023; Ho al., 2022; Polyak et al., 2024; Singer et al., 2023), controllable video generation with additional control signals, such pose (Ma et al., 2024a), depth (Esser et al., 2023; Xing et al., 2024b), and sketch (Yuan et al., 2024; Meng et al., 2024; Xing et al., 2024a), has been developed to generate videos adhering to users' intentions more precisely. Camera motion control has been explored through motion LoRAs (Blattmann et al., 2023; Guo et al., 2023; Kuang al., 2024), enabling video generation with specific camera movement patterns. For finer control, line of work has explored employing camera conditions through intrinsic and extrinsic matrix (Wang et al., 2024b), Plucker embedding (Bahmani et al., 2024; 2025; He et al., 2024; Li et al., 2025), background-poit trajectory (Feng et al., 2024; Wang et al., 2024b), point-cloud re-rendering (Yu al., 2024), or depth-based warping (Hou et al., 2024).
Diffusion-based video editing and inpainting. The field of video editing has broad applications. Early studies (Cao et al., 2023; Kawar et al., 2023; Wan et al., 2024; Meng et al., 2022; Liu et al., 2025a) developed training-free or fine-tuned text-driven editing methods on images. Some works (Cong et al., 2023) extend text-to-image models, where TAV Wu et al. (2023) achieved video generation through one-shot tuning. with later works (Ceylan et al., 2023; Chai et al., 2023; Ouyang al., 2023; Pumarola al., 2021; Qi et al., 2023) improving temporal consistency. Video inpainting is subtask of editing, which utilizes the user-specified mask sequences to edit the content in video. Previous works can be classified into two categories: non-generative methods and generative methods. Non- Non-generative methods (Hu al., 2020; Liu al., 2021; Zhou et al., 2023) facilitate pixel propagation using architecture priors. But they are limited to only being effective for partial object occlusions with random masks. With the development of generative models, some works (Bian et al., 2025; Zhang et al., 2023b; Zi et al., 2024) adopt the advanced text-to-video diffusion to improve their performance. They only focus on the content inpainting in video. In contrast, reformulate 4D) video creation as a video inpainting task and unlock the potential of video inpainting models for 4D reconstruction, enabling high-fidelity results with minimal additional training.
3 METHOD
The pipeline of our method is shown in Fig. 2. Given a video, we target the creation of dynamic 4D) video content, including camera retargeting (e.g., zoom, tilt, pan) and video content editing (e.g.. subject addition and modification). Different from previous works (Bai et al., 2025; YU et al., 2025) tuned on large-scale video datasets, we reformulate 4D generation as a specialized video inpainting task and fine-tune the model (Wang et al., 2025) to unlock its potential. In the following, we first discuss the details about the dynamic point cloud (in Sec. 3.1) and composite mask (in Sec. 3.2). Then we introduce our iterative tuning in 3.3. Finally, we present the temporal-packinn inference for multi-view video consistency in Sec. 3.4.
3.1 DYNAMIC POINT CLOUD
Given an input video V = [L..... IN- E Nx3x where N denotes the number of frames, our goal is to synthesize new video sequence that follows user-specifted camera trajectory. The
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Iterative tuning
Dynanic point doed
Compulation
inpainting
Model
Editig mesks
Updale
PAA
Blus
Comaro
repin ael
trier with Inojectory
frajectory
Training stage
Compoctte mests
gardas.
walking
UMTS
Ovipul
Figure 2: Overview of our method. We reformulate the 4D video creation as a video inpainting task. Left: given a video, we first generate the composite masks from the dynamic point cloud and feed them into the video inpainting model to unlock its 4D video creation capability. Right: To unlock the capability of generating 4D video with larger motion, first generate videos with small motion, then feed them into the model to improve the temporal consistency progressively.
dynamic point cloud serves as crucial intermediary representation that bridges the original frames and the novel camera views. Specifically, we use the off the-hhf- video depth estimator (Hu et al., 2025) to estimate per-frame depth map D {D+ - By combining the video frames and their depth maps, the point cloud sequence P [Pi] can be computed as follows:
K)),
(I)
where @ function that maps [L, D.J to 3D point cloud in the camera's coordinate system using K that represents the intrinsics of the camera described in (Chung et al., 2023; Jeong et al., 2025). Additionally. the camera motion is provided as a sequence of extrinsic matrices T = (Tı, Tx-1}. With these extrinsic matrices, can project the point cloud from each frame back to the camera plane using the perspective function v to render an image:
I (P,,K,
However, the rendered results are often incomplete, as a single monocular depth map is insufficient to reconstruct the entire scene, leading to occluded or missing regions. These missing areas can be identified during the rendering process, where a binary visibility mask E NXIX generated. Pixels with valid projections are marked as 1, while regions falling outside the original view due to camera motion are marked as O. Our method aims to leverage a video inpainting foundation model to fill in such masked regions, thereby enabling the generation of a complete 4D vidco.
3.2 COMPOSITE MASK
Apart from the aforementioned visibility masks, our method also supports 4D editing, which requires an additional mask to indicate the user-specified editing regions. In the following sections, we describe how we construct various types of masks and integrate them into composite mask dataset that serves as the supervision for training our video inpainting model.
Point cloud mask. It is hard directly use the original visibility masks in Sec. 3.1 for supervision, since the rendered frame Ić does not contain ground-trute content for the occluded regions. To overcome this, we adopt a double reprojection strategy (YU al., 2025) that reprojects the visibility masks back to the viewpoint of the input video. This allows us to obtain supervision masks that are spatially aligned with the original frames, and we can set the input video as the ground truth for training the inpainting model. Specifically, cloud P' using D' and re-render the view V by applying the inverse transformation T-1. This results in paired set consisting of the corrupted video V with the Mask M" to indicate the artifacts region and the corresponding clean video Va, both following the same original camera trajectory.
back-project the rendered views V into new point
Editing mask. For 4D video generation, content editing plays crucial role in practical applications. To leverage powerful inpainting priors and enable more flexible video editing, we adopt a content editing strategy inspired by prior work (Mou al., 2024). During training, randomly select
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region and generate the corresponding mask sequence. The mask for the first frame is set to '0', indicating that the first frame serves as the guidance for video synthesis. During inference, content editing performed by modifying the first frame, and the changes are subsequently propagated to the following frames.
Union mask. To enable both 4D completion and 4D editing tasks simultaneously, we combine the aforementioned two types of masks using a union operation to form a new composite mask.
After obtaining various tideo-mask pair data, randomly select three types of masks: point cloud masks, editing masks, and their union in the training stage. The conventional diffusion inpainting pipeline is adopt, which takes the corrupted video V and the corresponding occluded mask M" as input conditions and predicts the completed video V using standard flow matching loss (Wang al., 2025).
3.3 SELF-ITERATIVE TUNING
By leveraging powerful video inpainting foundation model as generative prior, we regard the 4D video creation as a video inpainting task. However, a vanilla video inpainting diffusion model has challenge in handling the hole video with a larger angle (e.g. > 40 degrees). The reason behind this lies in two key factors. Firstly, in our setting, the video inpainting methods typically perform fine-tuning on a single video with various masks, which restricts their generalization ability. Secondly, current video inpainting models lack robust 3D perception capabilities. Consequently, these methods struggle to generate large-angle scene reconstructions in videos while maintaining temporal consistency across frames. To generate the video with larger-angle content, we propose the self-iterative tuning, which enhances the 3D generation ability of the video inpainting model progressively.
N as defined Sec. 3.1, our pipeline initiates by generating multiple video-mask pairs {(V(k), ( using small viewpoints (e.g., 30 degrees). These pairs are used to perform one-shot tuning of the video inpainting model through Low-Rank Adaptation (LoRA), and optimized parameters
In detail, given a reference video I
W LORA arg min L (v*, .M AW),
(3)
where AW denotes the low-rank parameter updates. After tuning, we load the LoRA weight W LoRA and infer the video V with trained small angles. Subsequent iterations employ angle-progressive scheme: at each loop j, we generate a new masked video with larger angular ranges. The model then performs self-iterative pipeline through the recurrence relation:
i 2b (P) K, T.). = 1.,,1..,,NN1
N
(4)
V Iy.... ...... N-1
(5)
w 5) w(-1) LoRA + W (V. AW)
(6)
LORA
where (-))) denotes our geometric warping function that extrapolates viewpoints. The ry is the learning rate, and Loyele means spatial-temporal consistency MSE losses.
3.4 TEMPORAI-PPCCKING INFERENCE
After finishing the self-iterative tuning, we aim to achieve 4D video creation using various camera trajectories and the edited first frames. However, there is still a challenge 4D video creation: multi- view video consistency. In detail, our goal is to preserve the subject and scene consistency in generated multi-view videos. Previous works, such as Recaputre (Zhang et al., 2024a), RecamMaster (Bai et al., 2025), and TrajactoryCrafter (YU et al., 2025), only focus on the consistency between the input and generated videos rather than multi-view generated videos. Reangle-a-videe (Jeong et al., 2025) utilizes the image inpainting tools to improve the consistency. But it requires manual selection. In our work, during the inference stage, we propose the uamporal-packing strategy to maintain the multi-view video consistency.
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Ouipul Ieput video
kayak moving forward the
butterfly flapping its wings flwee.
View
panda is dumping snow on Mount Fuji.
View
Vie
A terracotta figurine is playing pipa
Input video
terracotta Figurins playing the pipa accompany dancing robot.
Figure 4: Gallery of our proposed method. Our EasyCreator enables achieving flexible and high- quality 4D video creation using the given camera trajectory and the edited first frame (2nd row). Additionally, it also supports the 4D video creation using various prompts in frozen camera ("exhibi- tion" in 4th row and "robot" in 6th row).
Specifically, as shown in Fig. 3, we first observe that the rendered hole videos from two different camera trajectories have the overlap areas (overlap mask in Fig. 3). Inpainting the same area in two forward passes will lead to regional inconsistency. To improve the coherency multi-view video,
after obtaining the generated video Va from EasyCreator,
camera trajectory T using then we calculate the area of inpainting in each frame and select the K frames in Va
Ovarig
Canera Iejuctery
F = arg-a-argmax M (7)
Cenect Inderery
Canaro THANTTYA
where S(.) notes calculation function. In the next inference for camera trajectory Thh we concatenate the selected frames' tokens with the hole video token along the temporal dimension:
Figure Motivation temponal in- ference. During the generation, there are existing overlaps (overlap mask) between various camera poses, which enables improving the consistency in multiple views.
Fémput +(patchify(((F)).
(8)
patchi lemporaly
where Tinput BX22NSSXC is the input of video inpainting model, and S = Hl x W. C'is the channel dimension for the latent diffusion model. E(-) notes pretrained 3D-VAE (Kong et al., 2024). Note that do not design any additional attention layers for feature fusion. In pretrained video inpainting model (Wang et al., 2025), self-attention is applied globally across all tokens within the spatio-temporal attention layers.

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video
GCD
ReCamMaster
Thaj Atti
Traj-Crufher
Ours
Figure 5: Qualitative comparison results with the sfate-of-thh-are methods. The results show that our EasyCreator exhibits 4D video creation with better consistency and camera movements.
EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
In our experiment, the open-sourced video generation model WAN-2.1 (Wang et al., 2025) is employed as the hase text-to-video generative model. We the LoRA (Hu et al., 2021) to finetune the model with the ranks of 128. During one-shot training, each video is input into the model as 512 512, and the video length is set 81. The training stage is conducted for 2000 steps with learning rate 1 x 10-5 and weight decay 0.1. For producing dynamic point cloud, depth sequences are evaluated from input video using the open-sourced depth estimator DepthCrafter (Hu et al., 2024), with empirically configured camera intrinsics. We optimize our model using PyTorch on a single NVIDIA A800 GPU for about hours. During inference, we employ the DPM solver (Lu al., 2022) with 30 sampling steps and a text- rext-guidance scale of 6.5. The LoRA weights are fixed at 0.7. Additional implementation details and evaluation metrics are provided in Appendix A.
4.2 COMPARISON WITH BASELINES
Qualitative comparison. We first evaluate the camera motion editing ability of our method with four baselines: Trajectory-Attentionn (Xiao et al., 2024b), ReCamMaster (Bai al., 2025), Trajec- toryCrafter (YU et al., 2025), GCD (Hoorick et al., 2024). The first method is diffusion- novel view synthesis method, and ReCamMaster and TrajactoryCrafter are state-of-the-art generative camera-retargeting methods tuned on large-scale video dataset. GCD is 4D novel view synthesis technique, which integrates implicit camera pose embeddings into video diffusion model. The visual comparison is shown in Fig. 5(left.. We can see that videos generated by GCD exhibit over-smoothed details and view misalignment issues. On the other hand, while TrajactoryCrafter and ReCamMaster demonstrate better pose accuracy, they struggle to produce high-fidelit frames. In contrast, method is capable of generating novel trajectory videos with high fidelity, remarkable 4D consistency, and precise pose control.
Additionally, to test the ability of 4D video creation, we conduct a comparison with a baseline approach. In detail, first edit the video using advanced video editing tool (Jiang et al., 2025), then feed it into the camera-retargeting baselines for visual comparison. In Fig. 5(right), we present the visual quality of the results produced by our method and the baselines. It's observed that the 41)
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Table 1: VBench results between ours and baselines. We collect comprehensive video benchmark with 40 real-world videos and 40 high-quality generated videos to evaluate the performance. Red stands for the best result, Blue stands for the second best result.
VBench f
Method
Subject Consis.1
Background Consis. 1
Temporal Flicker.
Motion Smooth.]
Overall Coesis
(Hoocick al., 2024)
0.7245
0.7041
0.1932
Trajectory-Atennti (Xino ol. 2024b)
0.7419
0.7821
0.7346
0.7528
0.2087
ReCamiMaster (Bai al., 2025)
0.8217
0.8457
0,8219
0.8523
0.2376
Trajectoryrratter
2025)
0.8632
Ours
0.9026
0.8931
0,8818
0.9242
0.2915
Table 2: Quantitative comparison with state-of-the-ae methods. We assess visual quality, camera accuracy, and view synchronization. Red stands for the best result, Blue stands for the second best result.
Method
Visual Quality
Camera Accuracy
View Synchronization
FID 4
FVD
CLIP-T
CLIP-F
RotEir
Transtir
Mat. Pix.(K)
FVD-V
CLIP-V
GCD (Hoorick et al., 2024)
73.92
368.44 -4
32.81
93.66
2.25
5.78
638.76
364.28
85.94
Trajsctory-Atteniio (Kino al., 2024b)
70.33
275.84
33.08
94.51
620.17
239.15
88.53
ReCamMasster (Bai al., 2025)
64.82
162.91
34.68
96.24
1.48
5.58
628.45
153.29
88.27
TrajectoryCrafter (YU et 2025)
61.57
154.23
35.27
96.15
1.43
5.52
635.25
148.71
87.42
Ours
58.26
145.71
35.63
96.62
1.37
4.47
705.34
119.52
89.87
video content generated by baseline methods has significant artifacts. On the other hand, our method capable of achieving better editing effects for 4D videos, along with smooth and accurate camera movements.
We perform three comprehensive quantitative assess- ments of the results obtained by our proposed method and the baseline. We also apply some state-of-the-art methods to Wan-2.1 (Wang al., 2025) and for fair comparison (See Appendix E.1). The user study is provided in the Appendix D. (I) Low-level metrics in the Kubric-4D dataset (Zhang al., 2024a). The video has a resolution of 576 384 and spans across 60 frames 24 FPS. We select the PSNR, LPIPS, and SSIM as low-level metrics to evaluate simi- larity between generated and ground truth novel views. The results are reported in Tab. 3. The results clearly indicate that our method outper- forms the baseline across all metrics.
Quantitative comparison.
Table 3: Comparison results Kubric-4D. Red and Blue denote the best and second best results.
Method
PSNR
SSIM
IPPS
GCD (Hoorick al., 2024)
14.21
0.398
0.612
Trjectory- Attention (Kian et al., 2024h)
14.65
0.426
0.587
ReCumMaster (Bai al., 2025)
15.03
0.453
0.561
TrajoctoryCrafte Crafter (Mark et al., 2025)
15.82
0.487
0.532
Ours
22.15
0.523
0.381
(2) VBench metries: We collect 40 real-world videos and 40 high-quality generated videos by advanced text-to-video generative models (Kong et al., 2024; Wang et al., 2025). For each vidco, we generate 5 different novel trajectory videos. Five metrics in VBench (Huang et al., 2023) are employed for more accurate evaluation (in Tab. 1)
following the previous work (Bai et al., 2025), we calculate the other metrics in Tab. 2. We assess camera trajectory accuracy by calculating rotation and translation errors, following methods from earlier research in camera-guided generative approaches (He et al., 2024; Wang et al., 2024b). For view synchronization, video frames from different viewpoints in the same scene, which we refer to as CLIP-V and FVD-V Notably, FasyCreator outperforms baselines across multiple metric dimensions, demonstrating its superior generative consistency and visual fidelity.
(3) Other metrics:
computed clip similarity scores and FVD between
4.3 ABLATION STUDY
Effectiveness of composite mask. To investigate the contribution of the composite mask during training, we conduct series of ablation studies it. The experimental settings are the same during the ablation. As shown in Fig. 6(a), when we remove the composite mask during the training stage, the results have artifacts and fail to follow the edited first frame ("French fries" on the ice). In contrast,
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Table 4: Quantitative ablation results. Red and Blue denote the best and second best results.
Visual Quality
Camera Accuracy
View Synchronization
Method
FID 4 FVD
CLIP-T 1 CLIP-F
RotErr
TransErr
Mat. Pix.(K) 1 FVD-V 4 CLIP-V
Wio composite mask tuning
78.27
153.28
30.81
94.21
1.56
5.26
518.21
155.47
85.25
W/o iterative tuning
86.29
197.24
36.58
92.74
1.49
4.93
589.29
204.81
81.26
W/o temporal pack stralegy
62.46
168.91
34,92
93.44
1.51
4.52
524.63
137.64
84.71
Ours
58.26
145.71
35.63
96.62
1.37
4.47
705.34
119,52
89.87
Engul video
Wil composilte mask
Ours
Input
Herathiv hunnn
Tmm
Figure 6: Ablation study"about composite mask (a) and self-iterativv tuning (b). Fig. 6 (a) demonstrates that our proposed composite mask not only keeps a smooth camera trajectory during the generation process but also enables the performance of the editing task. Fig. 6 (b) shows that the self-iterative tuning helps maintain a better temporal coherence in a larger camera motion angle.
our method enables creating a reasonable video with the subject while following the given camera trajectories. Additionally, we also perform a quantitative ablation study (in Tab. 4), Without the composite mask strategy, our method fails to achieve both the 4D video creation and editing.
Effectiveness of self-iterative tuning. We fur- ther assess the effectiveness of the proposed iter- ative tuning in Fig. 6(b) and Tab. 4. It is clearly observed that without iterative tuning, the gen- erated video has the challenge of maintaining temporal coherence (which is marked in orange boxes) in Fig. 6. This situation worsens when increasing the camera's 's motion angle. We an- alyze that the model lacks prior information in specific scenarios (e.g., in the room), which de- grades the generation capability of the video inpainting model.
Input video
Generated result (T
CAT
w/o tps (Tb)
OT ")
CAT
Figure 7: Ablation study of temporal-packing inference. T represents rightward camera mo- tion, while T turns both upward and rightward, exhibiting partial spatial overlap with T Un- inference strategy, our method keeps a better multi-vien consistency be- tween two trajectories.
Effectiveness of temporal-packie packing strategy. In Fig. 7, we show the results when there is a lack of temporal pack strategy during in- ference. We first generate the video using the camera trajectory To, Then we ablate the in- fluence of the temporal pack strategy using the camera trajectory T Since there are no extra constraints and conditions, the inpainted area in multi-viers video fails to preserve the consis- tency (which is marked in pink boxes). In contrast, our approach generates the multi-view video with consistent overlap content, which further demonstrates the effectiveness of the proposed strategy.
der the cemporal-packinn
5 CONCLUSION
In this paper, we present EasyCreator, a novel 4D video generation framework that reformulates 4D video creation as video inpainting task, generating more realistic and controllable results with minimal additional training. Specifically, we first generate the composite mask using the dynamic point cloud and double-reprojection strategy. To handle temporal consistency under large camera motion, we design self-iterative tuning strategy that gradually increases the viewing angles during training. To maintain the multi-view video consistency, the temporal-packing inference is introduced to enhance generation quality. Our method effectively leverages the prior knowledge of the video inpainting model without degrading its original performance, enabling the generation of 4D videos with consistent multi-view coherence.
L Limitations. The limitation of our method is discussed in the appendix.

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REPRODUCIBILITY STATEMENT
All quantitative tables, qualitative images, and video results in this work are reproducible and correspond to model outputs without manual editing or post-hoe alteration, except for minimal format conversion and compression. After the review process, we will release a partial public repository to support reproduction, including inference scripts, example data, and example videos. The datasets, configurations, and procedures used for training and evaluation are documented in Section 4.1 and Appendix 5. We will also provide fixed configuration files and random seeds so that independent rums can reproduce the visual results within expected stochastic variation.
ETHICS STATEMENT
Our work studies 4D video creation. To mitigate representational bias in demonstrations, we curated and display examples spanning different races, genders, and styles in the main text and appendix. All illustrative videos shown in this paper are sourced from publicly available web content; we respect the original licenses and terms of service and use the content solely for research purposes. We will not publicly release the dataset prior to completing the insertion of Al-generated watermarks and an ethics/content- safety audit. We explicitly prohibit harmful or deceptive uses of our methods and data, including deepfake attacks and other malicious generative behaviors. When any portion of our code made public, we will enforce visible and/or nateeaeedetectable watermarking during inference to help deter misuse. Any future releases will be accompanied by usage terms that forbid impersonation, harassment, or other malicious applications, and we will remove or restrict content that raises privacy, legal, safety concerns.
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Jusheng Zhang, Zimeng Huang, Yijia Fan, Ningyuan Liu, Mingyan Li, Zhuojie Yang, Jiawei Yao, Jian Wang, and Keze Wang. KABB: Knowledge-aware bayesian bandits for dynamic expert coordination in multi-agent systems. In Forty-second International Conference on Machine Learning, 2025c.
Kai Zhang. Lingbo Mo, Wenhu Chen, Huan Sun, and Yu Su. Magicbrush: A manually annotated dataset for instruction-guidee image editing. In NewrlPS, 2024b.
Yabo Zhang, Yuxiang Wei, Dongsheng Jiang, Xiaopeng Zhang, Wangmeng Zuo, and Qi Tian, Con- trolvideo: Training-fie freee controllable text-to-video generation. arXiv preprint arXhv:2305.13077, 2023a.
Zhixing Zhang, Bichen Wu, Xiaoyan Wang, Yaqiao Luo, Luxin Zhang, Yinan Zhao, Peter Vajda, Dimitris Metaxas, and Licheng Yu. Avid: Any-length video inpainting with diffusion model. arXiv preprint arXiv:2312.038816 2023b.
Haozhe Zhao, Xiaojian Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction-bar fine-grained image editing at scale. In NeurlPS, 2024.
Shangchen Zhou, Chongyi Li, Kelvin CK Chan, and Chen Change Loy. Propainter: Improving propagation and transformer for video inpainting. In Proceedings of the IEEÉ/CVF international conference on computer vision, pp. 10477-10486, 2023.
Junhao Zhuang, Yanhong Zeng, Wenran Liu, Chun Yuan, and Kai Chen. task worth one word: Learning with task prompts for high-quality versatile image inpainting. In ECCV, 2024.
Bojia Zi, Shihao Zhao, Xianbiao Qi. Jianan Wang. Yukai Shi, Qianyu Chen, Bin Liang, Kam-Fai Wong, and Lei Zhang. Cococo: Improving text-guided video inpainting for better consistency, controllability and compatibility. ArXiv, abs/2403. 12035, 2024.
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APPENDIX
Implementation detail
S2
Related work
S2
C
Preliminaries
S2
C.1 Rectified Flow (RF)
S2
C.2 Video inpainting diffusion models
S3
C,3 Video diffusion models
S3
User study
S4
E Additional Experimental Results
S4
E.1 Fair Comparison Using WAN-2.1 Backbone
S4
E.2 Results on Real-World Videos
S4
E.3 Results Longer Videos
S5
E,4 Results on challenge camera motions
S5
E,5 Results on multi-view video generation
S5
E,6 Results on various depth estimators.
S5
Visualized Comparision with baseline
S6
G Gallery of more results
S7
H Limitation
S7
I Social potential impact
S10
The Usage of Large Language Models
S10
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A IMPLEMENTATION DETAIL
In this section, we add more details about our framework. We select the Wan-2.1-14B (Wang et al., 2025) as our base model and fine-tune it using LoRA (14.3M). In temporal-packing inference, we select the top 10 frames as the prior. The prompts are generated by GPT4o (OpenAI, 2024). Each prompt contains about 15 words. For iterative tuning, we usually generate the corresponding rendered video and mask at intervals of 10 degrees. During inference, we need to segment the edited area using SAM2 (Kirillov et al., 2023). The first frame is edited by MagicQuill (Liu et al., 2025b). The videos in the main paper are all from the website.
B RELATED WORK
cuitusion-based video editing. Image editing is a broad and impactful field with diverse applications.
Early works (Ma et al., 2026; 2025e;d;b; 2024b; Zhang et al., 2025c;b;a; Ma et al., 2025c; Feng al., 2025; Meng et al., 2022; Yu et al., 2025) explore training-frees or fine-tuning-based methods to modify image attributes via text prompts. Subsequent approaches (Zhao et al., 2024; Hui et al., 2024; Zhang et al., 2024b; Yang et al., 2024b) advance instruction-bel
editing by training on curated datasets. A line of research explores additional control signals, such as masked regions (Zhuang al., 2024; Ju al., 2024), compositing content (Song et al., 2023), customized ID with reference images (Liu et al.. 2023; Kim et al., 2024), drag points (Cui et al., 2024). However, most of these works are limited to single editing tasks, making them inadequate for diverse real-world application scenarios. To address these limitations, unified frameworks (Xiao et al., 2024a; Han et al., 2024) are introduced to support various image editing and generation tasks. Recent advances in video editing can be categorized into two main approaches based on their underlying architectures. methods typically extend pretrained text-to-image models to the video domain. Tune-A-Video (TAV) Wu et al. (2023) pioneered this direction by adapting latent diffusion models for spatial-tempont generation through one-shot tuning. Subsequent works Qi et al. (2023); Ma et al. (2025a) improved temporal consistency through attention map fusion during inversion. Alternative approaches relying on Neural Atlas Kasten et al. (2021), dynamic NeRF deformation fields Pumarola et al. (2021); Chai al. (2023), optical flow, Yang et al. (2023); Cong al. (2023); Zhang et al. (2023a), feature aggregation (Jeong & Ye, 2023) significantly mitigate the temporal inconsistency issue. At the same time, they still suffer from artifacts when handling videos with large motions. Video-based methods leverage emerging video foundation models Yu et al. (2023); Yang et al. (2024c) to overcome some limitations of image-based approaches. Prior research efforts (Gu et al., 2023; Mou et al., 2024) demonstrate improved capabilities in motion transfer and editing by exploiting rich motion priors on single tasks. Recent works also investigate the merit of unified video generation and editing frameworks (Jiang et al., 2025).
amage-based
With the rapid development of diffusion models in the image and video generation domain, pre-trained video diffussinn models have demonstrated strong capabilities in novel view generation. AvatarArtist (Liu et al., 2025a) leverages diffusion models to predict a 4D representation of avatars, but it is not well-suited for general scenes. DimensionX (Sun et al., 2024) integrates a dedicated motion LoRA for dynamic new view synthesis, but its camera mo- tion is limited to few simple trajectories. RecamMaster (Bai et al., 2025) and TrajectoryCrafier (YU et al., 2025) employ the large-scale video dataset to tune the text-to-video diffusion model, which incurs significant computational costs. Some works (Jeong et al., 2025; Zhang et al., 2024a) use masked loss and regenerate videos using point cloud rendering and mask fine-tuning along custom camera paths. Despite having LoRA adaptation capabilities, they struggle with generating 4D videos with large camera motion. In contrast, our approach can generate editable, high-quality 4D videos with multi- consistency over a larger angle range.
Novel view synthesis of dynamic scenes.
C PRELIMINARIES
C.I RECTIFIED FLOW (RF)
Rectified Flow (RF) achieves high-quality generation with significantly fewer steps compared to
traditional diffusion models.It establishes a linear mapping between Gaussian noise distribution and real data distributions.govrrnee by ordinary differential equation (ODE): d7, v(Zz, t) dt, t E
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[0, where the velocity field is parameterized by a neural network rg.
During training, RF defines the noise injection process through a linear interpolation:X. = + X). with the corresponding differential form: dX, (X1 Xo) The objective is to minimize the squared error between the true derivative and the predicted velocity:
min L. E [I[
Xo) ( t)]1 dt.
(9)
In the sampling process, the ODE is discretized and solved using the Euler method. Starting from a Gaussian sample ZIN NO, I). the model performs iterative updates:
Zt Ze, + (4+-1 12) vo v ZZe 1z):
(10)
until the final output Zio is obtained.
C.2 VIDEO INPAINTING DIFFUSION MODELS
Based on the latent diffusion model, we use 3D (spatial-temporal) attention module to enable encoding and learning in the spatial and temporal dimension. This module is responsible for capturing temporal dependencies between the same spatial locations across different frames, extending the latent diffusion model into a video latent diffusion model. Therefore, the core layer of our model structure is composed of Transformer-basee diffusion model (DiT), where each DÝT module consists of 3D (eaatal-tempora) attention, and cross-attention modules. Specifically, we directly apply the pre-trained spatial module weights of latent diffusion models (LDMs) initialize the spatial layers of our network, and freeze these spatial layer weights during training to maximize the inheritance of the powerful generative capabilities of LDMs. Additionally, our video generation model adopts the Rectified Flow framework for the noise scheduling and denoising process. A deterministic mapping along straight path used to achieve more efficient and stable generation while maintaining high-quality sample outputs.
C.3 VIDEO DIFFUSION MODELS
Following pioneering Latent Diffusion Model (Chai et al., 2023), video diffusion models first compress the imput video V in pixel space into latent space x E(V utilize pretrained encoder E, where the latent space x can be reconstructed back to pixel space video by a decoder DD The encoder & and decoder 'D are built with causal 3D convolution blocks which and multi-framm videos into the same latent space. The size of a video latent æ is F x C' x W x H, where F, C, W, Hl stand for the video length, latent channels numbers, width, and height, respectively,
encode single-frame frame images
Recent video diffusion models (Wang et al., 2025) leverage flow matching to formulate the diffussion and denoising process in the latent space. During straining, timestep E [0, 1] is sampled from logit-normal distribution, and the intermediate latent 2; is defined as the linear interpolation between image or video latent Ij and a random noise xo E _N I) as
Ir tx (1 f)2r--
(11)
The velocity is further defined as
dr:
U1- di
x1 To
(12)
The diffusion models (Wang et al., 2025; Kong et al., 2024) take intermediate latents 3, as input and are trained to estimate the velocity v, using mean squared error loss.
min Es ||u vo (s,,t,)) #
(13)
where p is embedding the text description for the input clean video.
The inference stage starts from Gaussian noise 2o. then the pretrained diffusion model gradually
removes the noise in N discrete timesteps = t..... to as zt.-, = It, + (t--1 - t, t.) Finally, the predicted latent in is decoded to pixel space by the pretrained decoder D.
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Table User Study Scores. User study scores report the average rank (I=best, 5=worst) for all the methods; lower is better. Red and Blue denote the best and second best results
Method
Motion Pres.
App-I
Text Align.L
Overall
(Hoorick et al,, 2024)
4.783
4.642
4.956
TinjectoryAAteattin (Xiao et al., 2024b)
4.283
4.874
4.642
4,192
ReCamMaster (Bai et al., 2025)
3.350
3.086
3.205
3.883
TrajectoryCrafier (Mark al., 2025)
2.326
2.417
2.215
2.284
Ours
1.132
1.327
1.143
1.118
D
USER STUDY
To achieve more comprehensive evaluation of human preferences in video quality, perform user study with four aspects. Motion preservation assesses the camera motion's s adherence between given camera trajectories and generated ones. Appearance diversity measures the diversity according to the reference video. Text alignment means the semantic alignment between generated videos and prompts. Overall assesses the subjective quality of the generated videos. We invite 20 volunteers to provide the human feedback. The questionnaire includes 30 cases about our method and other baselines. The volunteers are asked to rank the video clips in the performance of various camera retargeting methods. (The smaller the score, the better; point is the best.). Then, we calculate the average result for each bascline. The results are reported in Tab. 1. Our method present the best performance in seven metrics of objective evaluation and user study.
E
ADDITIONAL EXPERIMENTAL RESULTS
E. FAIR COMPARISON USING WAN-2.1 BACKBONE
For fair comparison, provide the comparison with previous fitting methods in Tab. 2. ReCap- ture (Zhang et al., 2024a) and Reangle-A- Video (Jeong et al., 2025) are all popular fitting- based approaches, and we re-implemented them using WAN-2.1 for fair comparison. The results demon- strate that our EasyCreator consistently surpasses prior fitting methods across all quality metrics.
A-Vidoo
Table 2: Comparison using WAN-2.1 backbone. (re- (re-implemented baselines). We select two SOTA approaches, ReCapture (Zhang et al., 2024a) and Reangle-A. Video (Jeong et al., 2025) for fair comparison. Red stands for the best result, Blue stands for the second best result.
Method
Visual Quality
Camera Accuracy
View Synchromization
FID FVD J. CLIP-T f CLIP-F 1
RotEr
TransErr
Mat. Pix.()
FVD-V
CLIP-V
ReCapture (Zhang et al., 2024a)
65.26
177.45
33.62
95.41
1.54
5.62
633.78
155.29
88.41
Reangle-A. Video (Jeong al., 2025)
63.19
Ours
58.26
145.71
35.63
96.62
1.37
4.47
705.35
119.52
89.87
E.2 RESULTS ON WORLD VIDEOS
In Tab. 3. we show the quantitative comparison results real-world videos, which demonstrate that our approach achieves better performance in various metrics.
Table 3: Quantitative comparison real-world videos. We assess visual quality, camera accuracy, and view synchronization. Red stands for the best result, Blue stands for the second best result.
Method
Visual Quality
Camera Accurncy
View Synchronization
J
CLIP-P
RotEin
Transtin J Mat. Pi.iK)
FVD-V J CLIP-Y
ReCapture (Zbang al., 2024a)
71.28
280.92
32.15
93.65
2.21
5.71
615.31
244.27
87.61
Reangle-A-Ve A-Vico (Joong et al., 2025)
70.62
277.46
32.84
93.87
616.83
242.91
Trajectory-Atentiion Attentio (Xino et al., 2024b)
71.35
280. 18
32.11
93.57
2.20
5.72
614.82
243.88
87.49
ReCumMasttr al.,
TrajectoryCratter (YU et al., 2025)
62.49
159.17
34.33
95.22
1.48
5.58
629.48
153,56
86.47
34.69
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E.3 RESULTS ON LONGER VIDEOS
To further evaluate the performance on longer videos(>>0 (3=30 frames), we also select 1000 samples with longer videos for comprehensive assessment. As shown in Tab. 4, the results demonstrate that our method achieves superior performance.
Table 4: Comparison results based longer videos(>330 frames). We assess visual quality, camera accuracy, and view synchronization. Red stands for the best result, Blue stands for the second best result.
Method
Visual Quality
Accuracy
View Synchronization
FID |
FVD
CLIPTT
CLIP-F
RotEx
Transtir
Mat. Pix.(K)
FVD-V 4 CLIP-V
ReCapture (Zhang al., 2024a)
85.83
295.84 84
13.08
74,51
4.15
6.65
600.17
259.
73.53
Reungle-A Video (Jeong al., 2025)
86.12
296.17
12.87
74.32
4.42
7.03
598.62
258. 94
73.26
Trajectory-Attenn (Xino et al., 2024b)
85.49
294.72
13.25
74.67
4,28
6.89
602.45
257.83
73,91
ReCamMaster (Bai al,, 2025)
80.12
182.91
19.68
81.24
3.48
6.58
608.45
168. 29
73.27
TrajectorCrrffes (YU al., 2025)
77.37
81.15
Ours
74,06
165.71
20.63
81.62
3.37
6.47
685.34
134.52
74.87
E.4 RESULTS ON CHALLENGE CAMERA MOTIONS
To further evaluate the performance and robustness our proposed model, we select 2000 samples from Web Vid dataset, including many challenge camera motion videos (e.g., larger camera mo- tions (> 50 degrees), more challenging viewpoints (backside, top-down, bottom-up), faster camera movements). In Tab. our EasyCreator achieves state-of-the-art results across all quality metrics, substantially outperforning previous methods.
Table 5: Quantitative comparison on challenge camera motion videos (including challenging viewpoints, larger camera motions, and faster camera movements). We assess visual quality, camera accuracy, and view synchronization. Red stands for the best result, Blue stands for the second best result.
Visual Quality
Camera Accurncy
View Synchronization
Method
FID 1 FVD
CLIP-T
CLIP-F
RotEn:
TransEn J Mat. Pix
FVD-V
CLIP-V
ReCapture (Zhang al., 2024a)
79.04
283.49
27.13
80.32
2.85
6.73
538.27
320.89
73.48
Reangle-A-
A-Vide SeVigle--VVide (Jeong et al., 3025)
81.47
271.68
26.93
77.69
2.71
543.71
325.38
72.16
Trajsctory-Attenn (Xino et al., 2024b)
75.26
312.95
29.17
80.72
2.68
6.73
529.47
276.08
72.53
ReCamMaster (Bai al., 2025)
73.87
185.52
28.07
81.37
534.81
TrajectoryCraher (YU et al., 2025)
71.26
174.03
31.15
81.43
2.36
5.68
551.74
169.54
73.01
Ours
65.32
168.42
35.17
88.49
1.88
5.47
597.71
136.29
80.36
E.5 RESULTS ON MULTI-VIEW VIDEO GENERATION
We also conduct comparison experiments with multi-view video generation method to evaluate the performance of temporal ceaporr--packing inference strategy, and the results are shown in Tab. 6.
Table 6: Comparison with previous multi-view video generation approaches. Since the CAT4D and 4Real- Video do not release the code and checkpoints. We provide the quantitative comparisons with SynCammaster (Bai et al., 2024) for multi-view comparison. Red stands for the best result.
Method
Visual Quality
Camera Accuracy
View Synchromization
FID | FVD | CLIP-T CLIP-F f
RotEr | TransFir 4 Mat. Pix. f FVD.V CIIPPVV
Syn ConCammatter (Bai et al., 2024)
65.23
148.61
34.81
95.73
1.44
4.62
703.26
122.14 14
88.76
Ours
58.26
145.71
35.63
96.62
4.47
705.35
119.52
89.87
E.6 RESULTS ON VARIOUS DEPTH ESTIMATORS
We evaluate the performance using various depth estimators in Tab. 7. The performance drops slightly compared to our original results, but remains strong. This is because our method uses the proposed mask composition strategy, which helps better utilize the prior from the video inpainting foundation
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Input video
View 1
6CD
ReCanstaster
Traj-Attn
Tri Crafeer
Ours
Figure 1: More comparison results.
model. As result, even when the input masks are not very accurate with the erroneous depth maps, method still produce reasonable results. This shows that our method is robust to imperfect masks.
Table 7: Comparison using using various depth estimators. We select two SOTA approaches, Marigold (Ke et al., 2025) and Depth Anything (Yang et al., 2024a). Red stands for the best result, Blue stands for the second best result.
Method
Visual Quality
Camera Accuracy
View Synchronization
FID FVD
CLIP-T
CLIP-F1
RotEm
TransErr
Mat. Pix.(K)
FVD-V 1. CLIP-V
Marigold (Ke et al., 2025)
63.78
173.54
32.45
93.27
1.98
6.15
676.21
142.36
85.92
Depth Anything (Yang al., 2024a)
Ours
58.26 145.71
35,63
96.62
1.37
4.47
705.35
119.52
89.87
Table Ablation study with uncertainty estimation (UC), Red indicates the best performance.
Visual Quality
Camera Accuracy
View Synchromization
Method
FID
FVD | CLIP-T CLIP-F
RotEir 4 TransHm 4
Mat. Pix. + FVD-V J
CLIP-V 1
Ours
58.26
145.71
35.63
96.62
1.37
4.47
705.34
119.52
89.87
Ours+UC
56.84
136.29
39.76
99.72
1.18
734.62
112.84
91.02
F VISUALIZED COMPARISION WITH BASELINE
In Fig. 2. provide more comparisons to evaluate the performance of the proposed method. It apparent to discover that there an temporal inconsistency in previous works. In contrast, our approach could effectively address the issue of temporal consistency.
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Input video
View
View
GCD
ReCamMarter
Tra Jrhn
Traj-Crafher
Figure More comparsions. The prompt view is "a robot is dancing.". ". The prompt in view is "there are a porcelain and pottery figurine in the museum.".
Figure 3: Limitation of our EasyCreator
G GALLERY OF MORE RESULTS
We show more 4D video creation results produced by our method in an MP4 file, which can be found in the file: demo mp4.
H LIMITATION
The limitation of EasyCreator is inherited from unreal first frame editing and inaccurate video segmentation. Since this problem relates to the base model we are using, advancements in editing and segmentation models could rectify it. Additionally, method fails to handle free-style input video due to the capability of the pretrained foundational model, as shown in Fig. 7.
As a fitting- based method, our method optimizes for a specific video. Compared to tuning methods, is more time-consuming but requires fewer resources.
Due to the use of the WAN model as the base model, which has a large number of parameters, optimizing with LoRA will take longer time (2 hours). This issue will be improved with better models and more accelerated strategy in the future.
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Input video
W/o text prompt
W/o first frame editing
Ours
Time
Figure Ablation study about text description and first frame editing. The text prompt "A Captain America stands in the middle of golden wheat field, holding and playing an acoustic guitar." Using only the reference image yields significantly better results, but still falls short compared to our method, which combines both modalities.
Input video
Ours
PaintScene4D
Ours
Input video
PaintScene4D
A golden retriever dog walking through an ancient
fox building sandcastle on a beach in the evening.
monkey sitting in a wooden treehouse, playing Rubik's cube
A white rabbit nábbling on plants in flowe best
Figure 5: Comparsion with PaintScene4D. We use WAN-2.1-T2V-14B to generate the video and VIPE Huang et al. (2025) get the camera pose.
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degree
degree
50 degree
80 degres
W/ iterative tuning
Ours
Figure 6: More ablation study about self iterative tuning. is easy to observe that when we remove the self-iterative tuning strategy, the quality and performance of the generated results degrade lot.
Input video
Output video
Input video
Output video
Input video
Output video
Figure More cases. We input the videos with slight motion to evaluate our ability.
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1 SOCIAL POTENTIAL IMPACT
The introduction of EasyCreator has the potential to revolutionize the way individuals and organiza- tions create and edit 4D video content. By enabling users to generate high-quality 4D videos from single monocular video input, democratizes access to advanced video production tools, making feasible for non-experts to engage in complex multimedia projects. This can empower educators. content creators, and small businesses to produce engaging visual materials that previously required substantial resources and technical expertise.
Additionally, EasyCreator's capabilities for content editing through prompt-basee -haeet interactions foster creativity and innovation, allowing users swiftly implement changes tailored to their vision. This can lead to enhanced storytelling techniques in fields such as education, entertainment, and marketing. where immersive content captures audience attention more effectively.
From a broader perspective, the ability to generate and edit 4D videos can contribute positively to sectors like virtual reality, gaming, and remote collaboration. As these technologies become more integrated into our daily lives, they enhance social interactions and facilitate richer experiences, ultimately promoting a more connected and informed society.
Overall, EasyCreator not only advances technological boundaries but also holds significant promise for fostering creativity, accessibility, and engagement across various fields, thereby driving social impact in the digital age.
J THE USAGE OF LARGE LANGUAGE MODELS
In this paper, the usage of the LLM mainly falls into the following aspects:
Grammar checking and format optimization: In the paragraphs of the paper, LLMs are used for grammar error checking and format checking of charts and graphs.
Language polishing: The text description part of the paper LLMs to polish and optimize the language expression.
All authors are responsible for the content generated by the LLMs.
S10


Reviews:
[{"review_id": "40zdmmgcQn", "reviewer": "Reviewer_DsCd", "summary": "The paper introduces EasyCreator, a framework that reformulates 4D video generation and editing as a video inpainting task. It adapts a pre-trained video inpainting model (Wan2.1) using LoRA and novel training/inference strategies (self-iterative tuning, temporal-packing) to generate novel-view videos and support content editing from a single input video. The empirical results show state-of-the-art performance in terms of quality and consistency.", "questions": "1. The self-iterative tuning strategy uses the model's own predictions from a smaller camera angle to train for larger angles. What mechanisms are employed to mitigate error accumulation or hallucination propagation from earlier, potentially less-stable stages into the subsequent training data?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. High-Quality Results: The framework successfully unifies novel-view synthesis and prompt-based content editing in a single pipeline, showcasing superior quantitative and qualitative results against existing dynamic 4D methods.\n\n2.Effective Adaptation: Cleverly leverages a powerful existing video inpainting foundation model, requiring only lightweight fine-tuning (LoRA) for a new, complex 4D task.", "weaknesses": "1. Limited Technical Originality: The core pipeline—using geometry (reprojection/depth) to define masks for a subsequent video inpainting/completion step—is a common two-stage paradigm in 3D (Text2Nerf [1], Lucid Dreamer [2], text2room [3]) and 4D generation. Extending this concept to 4D video inpainting is not a sufficiently original technical contribution.\n\n\n2. Overclaiming Scope: The use of the term \"4D Creation\" is misleading. The method relies on a 2D video inpainting model conditioned by geometric priors derived from an initial reconstruction. It lacks an explicit, continuous 4D representation  to inherently enforce multi-view geometric consistency across all synthesized views. A more accurate description of the task is Camera-Controlled Video Generation, as the fidelity relies on the inpainting model's hallucination capability rather than a proper geometric rendering function.\n\n\n[1] Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields\n\n[2] LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes\n\n[3] Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models", "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": "9NC2zWt7Cz", "reviewer": "Reviewer_6ax8", "summary": "This paper proposes EasyCreator, a method that generates multi-view 4D scene representations from input videos while enabling object editing within the scene. Built upon the Wan2.1 model, EasyCreator reformulates 4D scene generation as an image inpainting task and controls editable regions through an editing mask. In addition, an iterative tuning strategy is introduced to handle large camera motions, and temporal-packing inference is employed to ensure scene consistency across multi-view camera videos. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of camera motion accuracy, video generation quality, and editing capability.", "questions": "Please see the weekness.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- This paper integrates camera-controlled video generation and video editing into a unified framework, leveraging the power of generative models to reformulate the task as an image inpainting problem, thereby ensuring 3D consistency in video editing.\n- The paper is well written and logically structured, making it easy for readers to understand.\n- Experimental results show that the proposed method outperforms existing approaches in terms of video quality, camera accuracy, and editing capability. Moreover, it achieves better performance than the baselines of simple video composition–based editing methods and camera-controlled video generation methods.", "weaknesses": "1. What is the relationship between text prompt control and reference image control during the editing process? If only the first edited frame is provided without a textual description of the editing instruction, or if only the textual description is given without the first edited frame, how would the editing results differ in each case? Do both the text and the reference image influence the editing outcome?\n2. The authors should discuss the performance of their method in challenging scenarios, such as when the input video involves large camera movements or when fast motion within the scene leads to inaccurate depth prediction and thus imprecise dynamic point clouds. It is recommended that the authors analyze how their method performs under these conditions.", "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": "wAvvbz6D2c", "reviewer": "Reviewer_Urtw", "summary": "This paper proposes EasyCreator, leveraging a powerful video inpainting model to enhance the 4D generation/editing. Formally, the double-reprojection strategy is used to synthesize warping masks, while random editing masks are used for practical editing. The authors further proposed self-iterative tuning to address large viewpoint changes. The temporal-packing inference is used as a memory strategy to confirm the multi-view consistency. This paper includes detailed experiments to verify the effectiveness of the proposed method.", "questions": "The authors should clarify the contribution of this work. I am also looking for the response to address the concerns about  \"self-iterative tuning\" and \"temporal-packing strategy\".", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The presentation and overall layout of this paper are clear.\n2. This paper has detailed and comprehensive experiments, including quantitative and qualitative ablation studies, and convincing user studies.\n3. The proposed method enjoys good performance for 4D editing as shown in the supplementary and paper experiments.", "weaknesses": "1. The paper proposes a good approach for editing 4D generations through video inpainting, which is a promising direction. However, the core technical contributions are somewhat incremental. For instance, the key training strategy, double-reprojection masking, has been extensively utilized in prior works such as TrajectoryCrafter, See3D, and Vivid4D[1]. Similarly, the use of random editing masks is a standard technique commonly applied in image and video inpainting tasks. Regarding the other contributions, namely self-iterative tuning and temporal-packing inference, their effectiveness remains unclear and needs further clarification, as detailed below.\n\n2. A key limitation of video inpainting is that it fails to address large viewpoint generation, especially when the viewpoint change >90 degrees, leading to completely wrong masks. Intuitively, iterative inpainting methods (such as those employed in Vivid4D[1], which closely relates to this work but is not discussed) may help mitigate this issue, but at the cost of error accumulation. I guess the self-iterative tuning should be proposed to solve this problem, but the authors have not explicitly discussed this connection nor provided ablation studies that convincingly demonstrate its impact (Fig. 6b is particularly ambiguous). Fig. 6b shows that the self-iterative tuning merely removes an optional pillar rather than substantially improving the visual quality. Moreover, the self-iterative tuning is super time-consuming in this work (2h for Wan14B). How to ensure the efficacy, especially when multiple iterations might be necessary for handling large viewpoint variations.\n\n3. The temporal-packing strategy is proposed to solve the multi-view video consistency. This technique is effective and somewhat convincing, as shown in Fig7. But some detailed implementation of temporal-packing is missing. What does the \"area calculation function\" mean in Line306? It is also not specified whether the selected frames in the input tensor ($x_{input}$) require re-noising during inference. If re-noising is unnecessary, the model should have been explicitly trained under such clean multi-view conditions. Conversely, if re-noising is applied, it raises questions about how reliably these noisy frames can serve as clear references for inpainting. Moreover, some inconsistencies remain visible—for example, the pink box areas in Fig. 7 do not appear perfectly consistent, which calls for a deeper explanation.\n\n4. Several details about the experiments and implementation are not provided. It is unclear how many iterations EasyCreator’s self-iterative tuning requires in practice. The training data are not introduced, and the scale of the experimental evaluation is relatively limited, including only 40 videos.\n\n5. This paper misses some related citations and discussion:\n\n[1] Vivid4D: Improving 4D Reconstruction from Monocular Video by Video Inpainting. ICCV2025.\n\n[2] MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing. NeurIPS2024.", "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": "BTa3rK30sy", "reviewer": "Reviewer_FmC3", "summary": "EasyCreator converts a monocular input video into a dynamic point cloud via per-frame depth estimation, uses a double-reprojection strategy to derive visibility masks aligned with the original views, and combines these with user editing masks to form composite inpainting supervision that enables both 4D completion and 4D editing in a single framework.​ A self-iterative tuning scheme progressively increases viewpoint angles while reusing model outputs as training data to stabilize large camera motions, and a temporal-packing inference concatenates selected frames across trajectories in latent space to enforce multi-view consistency without adding new attention modules.", "questions": "I have highlighted the major questions in the weaknesses already, and I am summing them up below(I have summarised them shortly so that it is easier for the reviewer to quote the exact question they are answering. Please refer to the Weakness for the detailed problem and question asked.\n\n1. Why did you not compare or cite PaintScene4D, a very similar text-to-4D scene method that uses image-based inpainting and progressive filling? At minimum, can you provide a qualitative comparison using similar text prompts and compare your image/video outputs to theirs?\n\n2. You rely on the initial generated video being a static-camera video with only object motion. How do you ensure this in practice? Do you use specific prompts or procedures, what is the empirical success rate, and how sensitive is your pipeline to slight camera perturbations?\n\n3. If the initial warping or depth maps are poor, reprojection errors can cascade (bad depth → bad reprojection → worse depth for other views). How does your method detect and correct such cascading errors or inconsistencies during inference/training?\n\n4. When the camera moves only slightly, small gaps may be blurred rather than properly inpainted. How does your video diffusion model differ from an image diffusion model in this regime, and why should it handle small inter-frame gaps or slight camera movements better than image-based approaches?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "1. Reformulates 4D generation as video inpainting, directly targeting occlusion holes induced by camera retargeting and unifying 4D completion and editing within one framework.​\n2. Composite mask design (visibility via double reprojection plus user editing masks) provides flexible supervision that aligns with ground-truth content and supports practical edits.​\n3. Self-iterative tuning progressively increases camera angles to enhance temporal stability and large-view robustness without heavy re-architecture or large-scale retraining.​\n4. Temporal-packing inference enforces multi-view consistency by concatenating prior frames in latent space, leveraging existing spatio-temporal attention without extra fusion layers.​", "weaknesses": "1. I see that the paper doesn’t compare against a very similar method named PaintScene4D, which is also a Text-to-4D Scene generation method, but instead of video-based inpainting, they use image-based inpainting and progressively fill up the scene. Seems like PaintScene4D doesn’t have the code released, but I expect at least a qualitative comparison with similar text prompts shown by the work, and compare against their image and video results. The paper doesn’t even cite this work in its related works, which needs to be rectified. \n2. I see that you rely on the fact that the initial video generated is a static camera video with only object motion and no camera motion. How do you achieve this? Any specific prompt to achieve this, and what’s the success rate? Would the method work if there were any slight perturbations? How does the method handle those error inconsistencies? \n3. In case of initial warping tends to be bad or depth maps are bad, I suppose the bad depth maps might lead to bad reprojection, and this can indeed lead to bad depth maps for another view, and so on, and the error might propagate throughout the network. How does this method resolve such errors, as I am pretty sure that having inconsistencies while depth estimation might exist?\n4. Regarding training from small camera views to large camera views for better training stability: In PaintScene4D, it is mentioned that they use a farthest-view sampling, where they sample views farther from the initial camera because that is when the diffusion model has enough room to inpaint sufficient new details. But when you move the camera slightly, the small gaps created might not task the diffusion model to edit a lot, and it might sometimes just blur those holes. How does Video Diffusion Model differ from Image Diffusion Model in this case?", "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)