Any-to-Bokeh: Arbitrary-Subject Video Refocusing with Video Diffusion Model
Authors: Yang Yang
Siming Zheng
Qirui Yang
Jinwei Chen
Boxi Wu
Xiaofei He
Deng Cai
Bo Li
Peng-Tao Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Computational photography
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Diffusion models have recently emerged as powerful tools for camera simulation, enabling both geometric transformations and realistic optical effects. Among these, image-based bokeh rendering has shown promising results, but diffusion for video bokeh remains unexplored. Existing image-based methods are plagued by temporal flickering and inconsistent blur transitions, while current video editing methods lack explicit control over the focus plane and bokeh intensity. These issues limit their applicability for controllable video bokeh. In this work, we propose a one-step diffusion framework for generating temporally coherent, depth-aware video bokeh rendering. The framework employs a multi-plane image (MPI) representation adapted to the focal plane to condition the video diffusion model, thereby enabling it to exploit strong 3D priors from pretrained backbones. To further enhance temporal stability, depth robustness, and detail preservation, we introduce a progressive training strategy. Experiments on synthetic and real-world benchmarks demonstrate superior temporal coherence, spatial accuracy, and controllability, outperforming prior baselines. This work represents the first dedicated diffusion framework for video bokeh generation, establishing a new baseline for temporally coherent and controllable depth-of-field effects. Project page is available at this website https://vivocameraresearch.github.io/any2bokeh/.

Published as a conference paper at ICLR 2026
ANY-TO-BOKEH: ARBITRARY-SUBIEC VIDEO REFO- CUSING WITH VIDEO DIFFUSION MODEL
Yang Yang 1.
Siming Zheng
Qirui Yang"
Jinwei Chen?
Boxi Wult
Xiaofei He
Deng Cail
Bo Li
Peng-Tao Jiang
'Zhejiang University
vivo Bluelmage Lab, vivo Mobile Communication Co., Ltd.
(a) Imput Vidoo
(b) Rocus Arbitrary Focal Plane
(c) Canerde Bolch Indersity
Figure I: Any-to-Bokeh enables users to customize the focal plane and adjust bokeh intensity. The yellow box indicates the focal plane, and the grayscale values in the image represent the distance to the focal plane, with higher values indicating closer proximity. K represents the bokeh intensity.
ABSTRACT
Diffusion models have recently emerged powerful tools for camera simulation, enabling both geometric transformations and realistic optical effects. Among these, image-based bokeh rendering has shown promising results, but diffusion for video bokeh remains unexplored. Existing image-based based methods are plagued by temporal flickering and inconsistent blur transitions. while current video editing methods lack explicit control over the focus plane and bokeh intensity. These issues limit their applicability for controllable video bokeh. In this work, we propose one-step diffusion framework for generating temporally coherent, depth-aware video bokeh rendering. The framework employs multi-plane image (MPI) representation adapted to the focal plane to condition the video diffusion model, thereby enabling it exploit strong 3D priors from pretrained backbones. To further enhance temporal stability, depth robustness, and detail preservation, we introduce a progressive training strategy. Experiments on synthetic and real-world benchmarks demonstrate superior temporal coherence, spatial accuracy, and controllabillity outperforming prior baselines. This work represents the first dedicated diffusion framework for vidco bokch generation, establishing new baseline for temporally coherent and controllable depth-of-fiel field effects. Project page is available at this website
INTRODUCTION
Recent advances in diffusion models have significantly advanced camera simulation, enabling controllable geometric transformations such as lens movement, zooming, and panning (Teng et al.) 2023 Shi al. 2024 Wu et al., 2024 Yin et al. 2023). These developments demonstrate their strong potential for reproducing realistic optical effects beyond traditional rendering pipelines.
Corresponding author. Equul contribution.

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Published as a conference paper at ICLR 2026
Building these advances, diffusion-bare based methods have recently been extended image bokeh rendering (Fortes et al., 2025 Zhu et al.) 2025). By leveraging generative priors, these methods achieve visually convincing blur transitions and highlight the feasibility of applying diffusion models to depth-aware, image-level optical effects. However, these methods focus on image inputs, with limited temporal modeling, so their applicability to videos is constrained.
Compared to image-based bokeh, video bokeh poses additional challenges remains limited in terms of temporal coherence and controllability. The limitations are two-fold. (i) Frame-by-frame extensions
of image methods (Sheng et al., 2024] Peng et al. 2022b a Lijun et al. 2018 Zhhu et al. (025) lack explicit temporal modeling and rely on imperfect depth estimates. Together with stochastic
noise accumulation in multi-step diffusion, this often causes temporal flickering and unstable blur boundaries, degrading video fidelity. (ii) Although recent video generative models (Blattmann) et al. 2023) provide strong 3D priors and cross-frame consistency, their bokeh-like effects are largely implicit and lack explicit control over the focal plane and bokeh intensity, limiting realistic and controllable video bokeh. These limitations highlight the need for dedicated framework for temporally coherent, depth-aware video bokeh.
To overcome these limitations, we introduce new framework that explicitly models scene geometry and leverages strong pre-trained priors to achieve temporally coherent, controllable video bokeh. A central challenge in video bokeh is ensuring smooth blur transitions across depth, particularly near object boundaries, where inaccurate depth often causes artifacts. To address this, we adopt multi- plane image (MPI) representation, which provides compact yet explicit encoding of scene geometry. By constructing MPI layers with a progressively widening disparity sampling function, our method captures fine details for in-focus regions while allocating coarser representation to out-of-focus areas, thereby enabling accurate and stable blur transitions across depth boundaries.
Building on this representation, we condition one-step video diffusion model on MPI layers, guiding the network to synthesize depth-aware bokeh effects that align with subject contours even in cluttered scenes. Unlike prior methods trained from scratch (Luo et al. 2024; Zhang al. 2019).
our framework leverages large-scale pre-trained video diffusion models (Blattmann et al. 2023).
whose strong 3D priors provide robust generalization across diverse scenarios and improve structural consistency, To further enhance temporal stability and detail preservation, we design three-stage progressive training strategy that (i) establishes accurate geometric guidance via MPI-based spatial and temporal modules, (ii) improves robustness and reduces flickering through extended temporal windows with data perturbations, and (iii) refines subject details using a VAE-based enhancement module. Together, these components form unified framework that directly addresses the core challenges of temporal coherence, geometric accuracy, and visual quality in video bokeh generation.
Extensive experiments both synthetic and real-world benchmarks demonstrate that framework produces high-quality, controllable video bokeh and significantly outperforms prior baselines. As illustrated in Fig. our method supports rendering from arbitrary video inputs and allows users to customize the focal plane and bokeh intensity. This capability opens up new possibilities for content creation, cinematic editing, and mobile post-processing. Our main contributions are as follows:
We propose the first one-step diffusion framework for controllable video bokeh, introducing an MPI-guided conditioning mechanism that injects explicit scene geometry enable spatially accurate, depth-aware blur synthesis.
We design progressive training strategy that improves temporal stability and detail fidelity, yielding realistic and controllable video bokeh across diverse scenarios.
Our approach achieves state-of fate-of-thee of-the-rrt in-focus boundary fidelity and temporal consistency, producing controllable video bokeh for arbitrary input videos with explicit control over the focus plane and bokeh intensity.
RELATED WORK
2.1 CAMERA SIMULATION DIFFUSION MODELS
Reference Guidance Models. Recent advancements in camera simulation have demonstrated the potential of leveraging reference videos to guide the motion and behavior of virtual cameras. A line of work (Zhao et al. 2024; Guo et al. 2024) encodes motion cues from reference videos via LoRA (Hu
et al. 2022), enabling the diffusion model replicate specific camera behaviors observed in the
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training set. In contrast, MotionClone (Ling et al. 2025) proposes a training- frree approach, directly extracting motion patterns from reference videos via spatial and temporal attention modules. Other approaches (Teng et al. 2023 Shi et al., 2024 Wu et al. 2024 Yin et al. 2023) require users to draw reference points to guide lens adjustments, offering more direct control over camera transformations.
Optical Effects with Diffusion. While earlier coffuusionbassed camera simulation methods mainly focused on motion modeling, recent studies have expanded to simulating optical effects such as bokeh. For example, BokchDiffusion (Fortes al., 2025) and Generative Photography (Yuan et al. 2025) generate high-quality bokeh effects from 2D images, capturing realistic defocus effects. These methods demonstrate the potential of diffusion models for simulating non- non-geometric camera behavior, but they are restricted to static images and lack temporal modeling, which limits their applicability to video tasks. contrast, our approach integrates optical effect simulation with pre-trained priors, enabling temporally consistent and controllable bokeh effects for arbitrary video inputs. This work, therefore, bridges the gap between image-based bokeh rendering and the video domain.
2.2 COMPUTATIONAL BOKEH
Training-Free -Free Methods. Early computational bokeh methods are training-free and rely on physically- based or image-based heuristics. Ray tracing (Pharr et al.) 2023 Potmesil & Chakravarty [981 Akenine Moller et al. 2019) produces realistic defocus effects, but requires full 3D geometry and is computationally expensive, which limits its practical applicability. Depth- methods (Wadhwa et al. 2018: Yang et al. (2016) apply scattering or gathering kernels to create spatially varying blur using estimated depth. Matting-baser methods (Shen et al. 201 (b) blur the background based on foreground masks. Although these methods do not require training, they often suffer from artifacts due to inaccurate depth estimation or segmentation errors, leading to unnatural blur transitions.
Learning-bass Methods. More recent approaches adopt machine learning to improve bokeh synthesis. For example, BokehMe (Peng et al.) 2022a) refines depth-based blur using classical methods (Wadhwa et al. 2018) along with neural networks, while models based Multiplane Images (MPI)(Zhou et al. (Pn8) et al., 2022b; Busam et al. 2019: Sheng et al., 2024) decompose scenes into layered representations to render depth- lwath effects. Other works explore adaptive
kernels or light field approximations (Srinivasan et al.] 2018 Kaneko 202T) to enhance bokeh effects. Deepleens (Lijun et al. 2018) introduces depth estimation network using depth estimation and foreground segmentation data to enhance the perception of foreground edges End-to-end models
trained on paired all-in-focus and bokeh datasets (Ignatov et al. 2020 Seizinger et al. 2025 Dutta
et al. 2021) show promise but are limited by dataset biases and fixed parameter simulati lation. They generally lack the flexibility to control focal planes or simulate custom bokeh intensities.
While image-based bokeh synthesis is well-studied, extending it to videos is challenging. Naive frame-wist methods often introduce temporal flickering and inconsistency. Our work addresses this by novel MPI- MPI-guided conditioning mechanism and leveraging pre-trained video diffusion models for consistent spatial and temporal bokeh rendering.
3 METHOD
An overview of our pipeline is shown in Fig. 2 a). Our framework performs video bokeh rendering in a one-step diffusion scheme, guided by MPI priors. The design comprises three components: (i) a Docal-plane-adaper MPI representation that provides geometry-aware spatial priors, (ii) a one-step video diffusion backbone conditioned on MPI-derived and user prompts that generates controllable and meticulous video bokeh rendering, and (iii) progressive training strategy that improves temporal stability and detail fidelity.
3.1 FOCAL-PLANE-ADAPPIN MPI REPRESENTATION
A central challenge in video bokeh is achieving realistic blur transitions, especially near object boundaries, where depth discontinuities often cause visible artifacts. Prior works (Peng et al.) 20226 Busam et al., 2019) discretize the scene into front-to-baut back layers with fixed depth values. While effective for simple rendering, such fixed discretizations are poorly aligned with the optics of bokch: blur radius varies nonlinearly with respect to the focal plane, and equal-depth intervals fail to capture
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(a) Any-to-Bokeh Pipeline
(b) Spatial Block
Cross-Atth
VAE Decoder
Encoder
CLIP
MPI-Attn
MPI Spetial Black
Fmbreedng
Self-Atth ttm
E
Temporal Block
A Adaptex
Threshold Function
HI
Canculinnata
Two key components of Any-to-Bokee (a) One-step video bokeh pipeline: receives input of any video and disparity relative to the focal plane to perform the bokeh effect. (b) MPI spatial block: uses the MPI mask M prompt MPI spatial block to guide bokeh rendering. The user-defined blur strength K injected through embedding.
Figure 2:
these variations. This mismatch often produces inaccurate blur transitions and artifacts around fine structures. Therefore, we introduce focal-plane-adapted MPI representation that explicitly drives the geometry prior relevant to bokeh formation. This adaptive scheme yields more accurate depth-dependent blur and ensures smooth transitions across subject contours. Specifically, the optics of the circle of confusion (CoC) relates the blur radius r to disparity:
K
= K dgl.
21
where K is the user-controlled blur strength; and d denote depth and disparity, respectively; and zf and y are the corresponding focal-plane values. Since r changes more rapidly near shallow focal planes and more slowly in distant regions, we sample disparity more finely near d and more coarsely farther away. Concretely, for each video frame, we predict a disparity map using a pre-trained depth estimator and define thresholds with threshold function:
h:
(2)
where 1/d, E (0, 1] acts as a factor, yielding finer sampling for shallow focus. Pixels are assigned to MPI layers using these thresholds, producing a plaaa-ppane-ddppeed MPI mask
{m, [d(m;) d h:). M
(3)
which highlights regions close to the focus and provides finer granularity around depth discontinuities. addition, we compute a normalized
-diferreeeefeeeece map VD from and d, (an implicit CoC
proxy per Eq. (T). Together, M,, Vp. and K constitute the geometry prior and user-guided controls that will condition the generator. Unlike fixed front-to-band to-back discretizations (Peng et al.] [0226]
Busam al. 2019), MPI is defined relative the focal plane, enabling focus-aware sampling and accurate boundary transitions.
3.2 ONE-STEP VIDEO BOKEH DIFFUSION ON MPI PRIORS
To avoid the temporal instability across frames caused by repeated iterations of the multi-step diffusion model and the high computational cost of multi-step inference, formulate the generation of video bokeh as a one-step diffusion guided by the MPI prior. Building on Stable Video Diffusion (SVD) (Blattmann et al.) 2023). remove stochastic sampling and adopt one-step U-Net that directly predicts the output frames.
The backbone is conditioned on three explicit signals: normalized disparity difference Vp (focus proximity); ii) blur strength K (bokeh intensity); iii) focal-plane-ddapted MPI mask M (geometry prior). This conditioning provides precise control over focus placement and bokeh intensity, while promoting frame-to-frand to-mma consistency.
3.3 MPI SPATIAL BLOCKS
To incorporate geometry into feature processing, we introduce MPI Attention, a gated attention mechanism inspired by (Li et al. 2023), integrated into the U-Net's spatial blocks, referred in
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Stage I: Chometry-Guidee Training
Stage 2: Temporal Refinement
Stage Detail Enhancement
Va
VATE
VAE Decodee
Var
VAE Enoodes
VA.E Docodar
VAE Enooder
VoT
Encoder
Vo
Decoder
Figure 3: Progressive Training Strategy: Stage I: Train the whole U-Net and adapters. Stage Refine the temporal block with the disturbance. Stage 3: Fine-tuning VAE decoder. We desaturated the colors in the same areas.
Fig. b)) MPI spatial blocks. Let Q (Q 1, 1..... Qi} be query tokens from the current block, VA A be visual tokens from the input video, and 7 learnable gate (initialized to zero). We modulate queries with K and guide attention with the focal-plane-adapee adapted MPI mask:
Q + tanh(?) TS (Attn(IQ + 2M (E(K)) a A)).
(4)
where ((K) is a Fourier embedding (Mildenhall et al., 2021 of K, ) and 2 (-(-) are lightweight MIPs,, TS(-) is a token-selection operator that selects the outputs corresponding to the tokens of Q after the attention operation, discarding the auxiliary tokens VA; [ denotes concatenation, and M = [1, M1 denotes the mask augmented by padding an all-ones matrix, which used in the masked-attention computation. We inject near-focus masks in shallow U-Net blocks to refine local transitions and wider-interval masks in deeper U-Net blocks for global context; mask resolutions are aligned to each block by bilinear interpolation. This design steers attention toward regions while maintaining global structure, enabling geometry consistent, contour-aligned bokeh.
3.4 PROGRESSIVE TRAINING STRATEGY
Although the one-step video diffusion model conditioned on MPI priors provides strong controllability, training such a model directly challenging. In practice, three issues must be addressed: (i) maintaining temporal consistency across frames, (ii) ensuring robustness against imperfect depth estimation, and (iii) preserving figh-frequency details that are often lost during encoding. To systematically tackle these challenges, adopt a three-stage progressive training strategy, as illustrated in Fig. 3
Stage 1: Geometry-Guided Training. We begin by fine-tuning the MPI spatial block, temporal block, and adapters on clean data. Training without stochastic noise encourages the network to fully exploit the local-elane-ddapted MPI mask, enabling it to learn spatially accurate, depth-aware blur effects. This stage allows the model to establish a reliable foundation for spatial geometry and to build initial temporal consistency across video frames.
Stage 2: Temporal Refinement with Disparity Noise. A key difficulty in video bokeh is robustness to imperfect or noisy depth estimation, especially around focal boundaries. To address this, we freeze the spatial MPI block and train only the temporal modules on longer sequences while injecting targeted disparity perturbations. Specifically, we apply elastic transforms (Buslaev al.) (0220) to distort depth locally, add Perlin noise (Perlin) 1985) to simulate natural inconsistencies, and use morphological operations to exaggerate boundary transitions. This perturbation setting prevents the spatial modules from being overwhelmed while teaching the temporal block to tolerate depth noise and reduce flickering. As a result, the model develops longer temporal memory and stronger resilience to wolld variations.
Stage 3: Detail Enhancement via VAE Decoder. Even with stable geometric and temporal modeling. fine details may be lost due to VAE-based compression. In the final stage, we fine-tune the VAE decoder and its adapter using clean data, introducing skip connection from the encoder to reuse spatially rich features (Parmar et al. 20024. To emphasize texture fidelity, we combine image-space L, loss with a gradient-based texture loss L.:
Σ (V. VVVV(a,)) + Z.U
V,,
L,
where Vn n and Vn 8 denote the predicted and ground truth frames. This loss encourages sharper edges and more realistic textures, thereby enhancing the perceptual quality of the bokeh effect.
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Through this progressive strategy, the model gradually learns spatial geometry, temporal robustness, and detail recovery, yielding temporally coherent, depth-aware, and visually realistic video bokch.
3.5 WEIGHTED OVERLAP INFERENCE STRATEGY
Naively splitting a video into overlapping segments and processing each segment independently can result in noticeable temporal discontinuities at the segment boundaries. These discontinuities often manifest as flickering. misaligned blur transitions, or mismatched bokeh effects, disrupting the smoothness and continuity of the video. To address this, propose weighted overlapping inference strategy (WOIS). Specifically, we divide the input video into P Pverlappingg segments, each containing 2L frames, with adjacent segments overlapping by exactly L frames. For the j-th frame in the overlap region, denoted as [i]. we compute the final output by combining adjacent frames from overlapping segments using a weighted average:
bbb % Vbl + (1 (5) - L + L}, i (1,2...., P).
(6)
where the weighting factor V is defined as:
7i = 2 1 1+ 11+ cos( + )
which smoothly blends the frames at the boundaries, ensuring gradual transition between segments. The bosine-based weighting function assigns higher weights to the central frames of each segment, reducing the influence of the boundary frames and minimizing visual artifacts the segment edges. This strategy enables our framework to process videos of arbitrary length, providing a robust solution to the problem of temporal flickering and boundary artifacts in video bokeh generation.
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Dataset. Currently, paired datasets for all-in-focus and bokeh videos are lacking. Existing com- putational bokeh datasets (Ignatov et al. 2020 Seizinger et al. [025]] Dutta et al. (021] primarily consist of image pairs but lack temporal consistency, and previous video-based works (Luo et al.) 2024) often omit foreground motion. To address this, we build upon prior work (Peng et al. 2025 and adopt synthetic approach to generate paired all-in-focus and bokeh video sequences. For accurate foreground extraction, we use objects from the video matting dataset (Lin et al.] [021). isolating them using the alpha channel for precise segmentation. We also incorporate the image matting dataset (Li et al. 2022 Hu et al. 2024) and collect 1,300 background images from the intern dataset and background dataset (Lin et al. 2021). In each video, we randomly select background and foreground clips, simulating world camera adjustments such focal plane and aperture changes. The foreground objects are moved along random 3D trajectories, each containing 25 frames. All videos in the dataset have resolution of 1024 576 pixels. Using ray-tracing-baaee method (Peng ct al. 2022b), we generate accurate bokeh effects, ensuring temporal coherence across frames.
Training and Inference. In this work, we use SVD (Blattmann et al., 2023 as our base model. During training, the process is divided into three stages, and the value of N in Eq. (2) is set to 4. Stage 1: We train using 4-frame video sequences with the Adam optimizer at learning rate of le-5. Stage 2: We train with 8-famme video sequences. using learning rate of 5e-6 and a depth perturbation probability of 0.5. Stage 3: We fine-tune tune the VAE decoder using the same learning rate in Stage 2. For all stages, the video resolution is set to 1024 x 576 with batch size of 1, and training is performed across 4 Nvidia H800 GPUs. During inference, we use the weighted overlap inference strategy where long videos are divided into clips of frames, with each clip having 4-frame overlap.
Test Setting. We validate our model on both synthetic and real-world datasets. First, we synthesize a test set of 200 videos with varying bokeh strength and focus planes. We report the following metrics: LPIPS (Zhang et al., 2018). PSNR and SSIM (Wang et al.] (2004) for image fidelity; VFID (Wang et al. 2018 with 13D features (Carreira & Zisserman, 2017) (denoted VFID-I) and FVD (Ge al. 2024) for video quality; and the relation metric (Luo et al. 2024) (denoted RM) for temporal consistency, which computes pixel-wise differences between adjacent frames. Additionally, calculate flow

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Table I: Quantitative comparison of Any-to-Bokeh. The best metric scores in each column are marked in bold for clarity." or "f" indicate lower or higher values are better.
Method
FD4
RML
VFID-14
FVD1
SSIM1
PSNR
LPIPS.
VEPI
Time
DeepLens
1.162
0.030
16.042
125.338
0.819
24.574
0.183
0.715
0.226
BokehDiff
0.660
0.021
7.395
65.678
0.834
27.525
0.127
0.859
0.799
BokehMe
0.536
0.013
8.633
39.102
0,936
27.992
0.060
0.937
0.103
Dr.Bokeh
0.522
0.011
6.097
32.710
0.950
31,273
0.046
0.863
2.729
MPIB
0.481
0.011
5.444
35.766
0.950
31,390
0.040
0.921
0.521
Ours
0.431
0.007
1,479
9.005
0.974
38.899
0.019
0.944
0.363
Disparity
All-in-Focus
BolshDift
BokehMe
Deepleens
MPB
Dr.Bokeh
Outs
Figure 4: Qualitative Results Real- World Video frames. To highlight the differences, zoom in on the red and green regions. Red arrows indicate incorrectly focused areas.
difference (FD) between predicted and ground truth frames using RAFT (Teed & Deng 2020). For real-world scenarios, we design VEPI, inspired by prior work (Joseph al. (007). to assess the model's 's ability to preserve detail at the edges of the focused subject. We apply the VEPI metric to
the DAVIS dataset (Perazzi et al., (016)), with implementation details described Appendix A.4 We evaluate 25-frame videos and compute the mean inference time.
4.2 RESULTS ON TEST DATASET
To verify the performance of our proposed method, we compare with four existing computational bokeh methods: Deepleens (Lijun al. 2018), BokehMe (Peng al. 2022a), Bokeh (Sheng et al.
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Imput
Output
Input
Output
Figure 5: Visualization of generated bokeh effects on DAVIS dataset. The yellow cross represents the focus subject. Please zoom in to see the details.
2024), MPIB (Peng et al. 2022b), and BokehDiff (Zhu et al. 2025). Specifically, to compare with traditional MPI-based bokeh methods, MPIB and Dr.Bokeh use the conventional approach, which discretize the scene into front-to-bach layers with fixed depth value. Only one prior work (Luo
ct al. 2024) addresses video bokeh, but it is not publicly available for comparison.
Quantitative Results. As shown in Tab. our method consistently outperforms all other approaches across all metrics. Specifically, Any-to-Bokeh achieves the lowest FD and the best R.M, demonstrating superior temporal consistency. Furthermore, Any-to-Bokeh surpasses the baselines in vidco quality, attaining the lowest VFID-I and FVD, along with the highest SSIM and PSNR, demonstrating its capability to produce high-quality image fidelity and video bokeh. In real-world scenarios, approach achieves the highest VEPI, demonstrating better blur transitions. Furthermore, compared to traditional MPI methods, our approach exhibits efficiency advantage in inference time.
Qualitative Results. To further highlight the visual advantages of our approach, we present three examples in Fig. 4 Any-to-Bokeh able to preserve detailed foreground textures, such animal fur and flowing hair, even under poor depth prediction conditions. Additionally, it produces bokeh effects that adhere optical principles across different depth planes, such as the cat's tail in the first column and the dog in the second column at farther distance. Traditional MPI-based methods, such as MPIB (Peng et al. 2022b) and Dr.Bokeh (Sheng al. (024). struggle to blend different depth planes naturally, resulting in color bleeding, as seen in the dog ear in the second column. BokehDiff (Zhu) jet al. 2025) and Deeplens (Lijun et al.) 2018)) can preserve some foreground details, but produce bokeh results that conflict with depth, such as the dog in the second column marked with red arrow. Moreover, BofehDiff consistently exhibits loss fine details in its results. BokchMc (Peng et al. 2022a heavily relies accurate depth information and fails to maintain fur details, seen in the in-focus subject the first and third columns when depth is inaccurate. Additional examples are and Fig. T3
provided in Fig. 10 Fig. 11
Fig. 12
Quantitative Comparison Under Inaccurate Disparity. As illustrated Tab. we introduce three types of perturbations to the disparity maps. As shown in Fig. 6 we report three metrics across three perturbation modes (II-IV). As the disparity perturbation increases from mode II to IV, our method maintains almost unchanged PSNR and consistently outperforms all baselines, demonstrating stable reconstruction capability under complex conditions, whereas competing methods exhibit varying degrees of degradation. In terms of video quality, our method maintains the lowest FVD across all perturbation modes, while other methods exhibit notable fluctuations or degradation (e.g., BokehDiff rises from 65 80, and MPIB shows sharp increase at mode IV). Regarding temporal consistency, our method consistently achieves the lowest FD and exhibits only minimal fluctuations even under severe disparity perturbations, whereas competing methods display an overall upward trend.
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Table 2: Mapping of Perturbation Modes.
Table 3: Results on human preference.
Perturbation
Gaussian Blur
Flastic Transform
Erosion
Baseline
Preference
Mode
Dilation
Ours DeepLens
96.9% / 3,1%%
Ours vs. BokehMc
77.1% / 22.9%
II
Ours vs. MPIB
62.9% / 37.1%
III
Ours Dr.Bokeh
77.8% / 22.2%
IV
Ours vs. BokchDiff
75.7% 24.3%
38
70
0.70
60
PSNR
52
34
TVD
50
0.65
0.60
RibsnDiff
Bo
Drkeh
0.55
30
Qurs
0.50
20
26
10
Figure 6: Evaluation on the synthetic test set with inaccurate disparity maps. The horizontal axis corresponds to the perturbation modes defined in Tab.
Computational Cost Comparison.
Table Results the computational cost comparison.
We provide a comparison of computa- tional cost in Tab. 4 All methods evaluated on single-frame inputs at a resolution of 576x 1024 the H800 GPU to ensure fairness. For BokehMe and Dr.Bokeh, which rely on custom CUDA-based renderers, full FLOPSs and parameter counts are not directly comparable. Therefore, for BokehMe, we report only the neural network component (marked with "x"). Despite using a larger backbone, our model achieves the shortest runtime among all methods. This efficiency stems from our end-to-end optimization and highly parallelizable architecture. Compared with the diffusion-based method BokehDiff. our approach is more efficient terms of parameter count and VRAM usage, and it also offers a clear advantage in inference time over the traditional MPI-based method MPIB and Dr.Bokeh.
Method
Time
Params (M)
GFLOPs
VRAM (GB)
BokchMc
0.103
*1.41
+66.77
1.4
MPIB
0.521
27.46
1418.50
3.3
Dr.Bokch
2.729
2.7
BokehDiff'
0.799
2459
3430
Ours
0.094
3620
User Study on Real-world Videos. We test on the DAVIS (Perazzi et al. 2016) dataset. As shown in Fig. 3 our method generates bokeh effects that follow optical principles and maintain strong temporal consistency. We conducted a user study on them to better evaluate different methods from a subjective perspective. We randomly selected 20 videos from the DAVIS dataset and rendered them using various methods. Each participant viewed two videos at a time: one generated by our method and one from randomly selected baseline. The videos were asked to choose the method that produced the most aesthetically pleasing bokeh effect based on their personal preference. they found it difficult decide, they were allowed skip making selection. The user study involved 51 participants and provided 1020 ratings across these video sets. The results Tab. indicate that our method was preferred over the others, demonstrating a higher human preference for the bokeh effects generated by our approach.
presented in random order. Participants
4.3 ABLATION STUDIES
We analyze the impact of each component in the Any-to-Bokeh framework on the synthetic test dataset (results in Tab. 5). evaluate the VAE's contribution to video detail quality (as shown in Tab, 6). and assess the effect of temporal refinement on model robustness (as shown Tab. 7
MPI Spatial Block Ablation. Lines 3 and 4 in Tab. show the ablation results for the MPI block. First, we remove the MPI module and use the original attention block, adding the blur strength (K) embedding directly the original SVD embedding. From the VFID and FVD metrics, observe that our model benefits significantly from the MPI blocks, leading to better video quality. Additionally, the MPI block enhances temporal consistency and fingle-frame quality, which are

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Table 5: Ablation study of Any-to-Bokeh module. "MPI": MPI spatial block. "OS": one-step inference schedule. "WOIS": weighted overlap inference strategy. "TR": : temporal refnement.
Variants
MPI
os
wois
TR
FDL
RML
VFID-11
FVDJ
SSIM
PSNR
LPIPS
#1 v
0.517
0.013
3.865
18.922
0.907
32.250
0.051
#2
/
0.540
0.013
4.209
20.743
0.905
32,035
0.052
43
0.551
0.013
4,521
21.941
0.905
31,936
0.054
84
0,573
0.013
4,930
23.828
0.903
31,551
0.059
#5
0.791
0.014
8.912
68.910
0.878
29.309
0.069
Table 6: Ablation study on FV.
Table 7: Ablation study on TR under noisy depth.
FV
FVDJ
SSIM
PSNR
LPIPS
TR
FD.
VFID-I.
FVDJ
SSIM
PSNR1
LPIPS,/
9.005
0.974
38.899
0.019
0.531
3.880
19.648
0,906
32,182
0.050
18.922
0.907
32.250
0.051
0.566
4.442
22.452
0,904
31,890
0.054
crucial for the bokeh rendering task. Additionally, we report results for two simple alternatives to MPI Attention, further demonstrating its effectiivness. Please refer to Appendix A.5.1
Effectiveness of One-Step Inference. We add noise to the input video features and retrain multi- step denoising model, comparing its performance with the one-step inference schedule. As shown in the 3rd and 5th rows of Tab. []] the one-step inference schedule substantially improves all metrics, highlighting its ability to achieve more temporally consistent and higher-quality bokeh effects.
Effectiveness of Weighted Overlap Inference Strategy (WOIS). By incorporating WOIS, our model achieves improved temporal consistency, with the flow difference (FD) decreasing from 0.551 to 0.540. Additionally, higher FVD and VFID scores indicate enhanced video quality. Furthermore, the PSNR also shows an increase, reflecting improved single-frame fidelity, More detail are provided in Appendix A.5.2
Effectiveness of Progressive Training Strategy. We begin the ablation study by evaluating the full model. In the second row in Tab. we remove the temporal block refinement (TR) from stage 2. resulting decrease in temporal consistency (FD: 0.517 vs. 0.540). This highlights the importance of temporal block refinement in maintaining temporal coherence across video frames. As shown in Tab. we alleviated the loss of nigh-freeueccy information by fine-tuning the VAE (FV), which effectively improves both single-frame consistency and overall video quality.
Ablation Study on Robustness. To test the contribution of TR to robustness, we introduce pertur- bations to the disparity in the test dataset using elastic transform (Buslaev et al. 2020). Gaussian blur, and morphological transformations. As shown in the last two rows in Tab. 7 TR leads to improvements across all metrics, with particularly noticeable gains in temporal consistency (FD) and video quality (VFID-I and FVD). These results demonstrate that training the temporal blocks with noisy data during the TR stage effectively enhances the model's 's robustness.
CONCLUSIONS
In this work, we propose the first one-step diffusion framework for controllable video bokeh. By incorporating an MPI-guided conditioning mechanism that injects explicit scene geometry, our method achieves higher-quality and more temporally consistent bokeh effects. Additionally, we introduce a progressive training strategy that enhances robustness and detail preservation, significantly improving bokeh quality. We hope our findings inspire further exploration of optical phenomena in editing models, driving advancements in their application to content creation and visual effects. By better understanding these phenomena, we aim enhance the realism and flexibility of future editing models, enabling more creative possibilities for the industry.
While Any-to-Boll BookB achieves significant improvements over existing methods, it still has two main limitations. First, due to limited computational resources, we did not investigate training with larger batch sizes. With more GPUs and a larger-scale training setup, the model could further improve its ability to handle complex scenes. Second, for long videos, cannot process the entire sequence in a single forward pass; instead, inference relies on repeatedly applying the WOIS strategy over overlapping segments, which sacrifices some efficiency.
Limitations:
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ETHICS STATEMENT
Our curated dataset is derived from publicly available matting datasets and an internal dataset. We use these assets in accordance with their original licenses and terms of use. No new data involving human subjects were collected, and all visualizations preserve privacy. We confirm that our method and experiments do not raise additional ethical concerns.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. To this end, we provide detailed descriptions of our methodology, data, and experimental setup throughout the paper and appendices. Following the blind review period, we plan to release the code for Any-to-Boked along with our model weights.
ACKNOWLEDGMENTS
This research was supported by The National Nature Science Foundation of China (Grant Nos: 62432014, 62402417, 62273301, 62273302), in part by "Pioneer" and "Leading Goose" R&D Program of Zhejiang (Grant No. 2025C02026), in part by the Key R&D Program of Ningbo (Grant Nos: 20247.1 15, 20257035), in part by Yongjiang Talent Introduction Programme (Grant No: 2023A-197-G).
REFERENCES
Tomas Akenine-Moller, Eric Haines, and Naty Hoffman. Real-time rendering. AK Peters/crc Press, 2019.
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 2023.
Benjamin Busam, Matthieu Hog, Steven McDonagh, and Gregory Slabaugh. Sterefo: Efficient image refocusing with stereo vision. In Proceedings of the IEEE/CVF international conference on computer vision workshops. pp. 0-0, 2019.
Alexander Buslaev, Vladimir I. Iglovikov, Eugene Khvedchenya. Alex Parinov, Mikhail Druzhinin, and Alexandr A. Kalinin. Albumentations: Fast and flexible image augmentations. Information, 11 (2), 2020. ISSN 2078-2489. doi: 10.3390/infol 1020125. URL /htttst:s....... htmpp::/wwitt wttp://wwww///.// htwp///wmmm.......mm www.mdpi. com/ 2078 2489/11/2/122
Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299 6308, 2017.
Sili Chen, Hengkai Guo, Shengnan Zhu, Feihu Zhang, Zilong Huang, Jiashi Feng, and Bingyi Kang. Video depth anything: Consistent depth estimation for super-long videos. arXiv preprint arXiv:2501.12377 2025.
Saikat Dutta, Sourya Dipta Das, Nisarg Shah, and Anil Kumar Tiwari. Stacked deep multi-scale hierarchical network for fast bokeh effect rendering from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2398-2407, 2021.
Armando Fortes, Tianyi Wei, Shangchen Zhou, and Xingang Pan. Bokeh diffusion: Defocus blur control in text-to-image diffusion models. arXiv preprint arXiv:2503.08434, 2025.
Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar. Jun- Yan Zhu, and Jia-Bin Huang. On the content bias in fréchet video distance. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
11

---

Published as a conference paper at ICLR 2026
Yuwei Guo, Ceyuan Yang, Anyi Rao, Zhengyang Liang, Yaohui Wang, Yu Qiao, Maneesh Agrawala, Dahua Lin, and Bo Dai. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. International Conference on Learning Representationss 2024.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Zhu, Yuanzhi Li, Shean Wang. Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations, 2022. URL http:ttt:/// http::://oppeeevie.cc ew. http:/iwwwwweeeeeeeeeeeeeeeeeeee furm??
id=n/eVKeeFFFF
Xiaobin Hu, Xu Peng, Donghao Luo, Xiaozhong Ji, Jinlong Peng, Zhengkai Jiang, Jiangning Zhang, Taisong Jin, Chengjie Wang, and Rongrong Ji. Diffumatting: Synthesizing arbitrary objects with matting-level annotation. In European Conference on Computer Vision, pp. 396-413. Springer, 2024.
Andrey Ignatov, Jagruti Patel, and Radu Timofte. Rendering natural camera bokeh effect with deep learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition Workshops, pp. 418-419, 2020.
Justin Joseph, Sivaraman Jayaraman, Rajadurai Periyasamy, and Simi V. Renuka. An edge preser- vation index for evaluating nonlinear spatial restoration in mr images. Current Medical Imaging Reviews, 13(1):58-65) 65, 2017.
Takuhiro Kaneko. Unsupervised learning of depth and depth-of-field effect from natural images with aperture rendering generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15679-15688, 2021.
Jizhizi Li, Jing Zhang, Stephen J Maybank, and Dacheng Tao. Bridging composite and real: towards end-to-end deep image matting. International Journal of Computer Vision, 130(2):246-666, 2022.
Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. CVPR, 2023.
Wang Lijun, Shen Xiaohui, Zhang Jianming, Wang Oliver, Lin Zhe, Hsieh Chih-Yau Kong Sarah, and Lu Huchuan. Deeplens: Shallow depth of field from a single image. ACM Trans. Graph. (Proc. SIGGRAPH Asia), 1-:61,, 2018.
Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steven M Seitz, and Ira Kemelmacher-Shlizeee Shlizerman. Real-time high-resolutie background matting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8762-8771, 2021.
Pengyang Ling, Jiazi Bu, Pan Zhang, Xiaoyi Dong, Yuhang Zang, Tong Wu, Huaian Chen, Jiaqi Wang, and Yi Jin. Motionclone: Training-free motion cloning for controllable video generation. In The Thirteenth International Conference on Learning Representations, 2025. URL https
net/ pepparrevveeennetfffooormaaaaaa. /tt.:/mmmdda333355gggg
Yawen Luo, Min Shi, Liao Shen, Yachuan Huang, Zixuan Yc, Juewen Peng, and Zhiguo Cao. Video bokeh rendering: Make casual videography cinematic. In ACM Multimedia 2024, 2024. URL
https::/cpperrevie
net. http:eeeeeweeeetteoooooooooooo/e
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-100 106, 2021.
Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, and Mun Zhu. One-step image translation with text-to-image models. arXiv preprint arXiv:2403. /2036, 2024.
Juewen Peng, Zhiguo Cao, Xianrui Luo, Hao Lu, Ke Xian, and Jianming Zhang, Bokehme: When neural rendering meets classical rendering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16283-16292, 2022a.
Juewen Peng, Jianming Zhang, Xianrui Luo, Hao Lu, Ke Xian, and Zhiguo Cao. Mpib: An mps-based bokeh rendering framework for realistic partial occlusion effects. In European Conference on Computer Vision, pp. 590-607. Springer, 2022b.
12

---

Published as a conference paper at ICLR 2026
Juewen Peng, Zhiguo Cao, Xianrui Luo, Ke Xian, Wenfeng Tang, Jianming Zhang, and Guosheng Lin. Bokehme++: Harmonious fusion of classical and neural rendering for versatile bokeh creation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(3):1530-1547, 2025. doi: 10.1 109/TPAMLL2224.5501777
Federico Perazzi, Jordi Pont-Tuset, Brian McWilliams, Luc Van Gool, Markus Gross, and Alexander Sorkine-Hornune A benchmark dataset and evaluation methodology for video object segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Ken Perlin. An image synthesizer. ACM Siggraph Computer Graphics, 19())287-226, 1985.
Matt Pharr, Wenzel Jakob, and Greg Humphreys. Physically based rendering: From theory to implementation. MIT Press, 2023.
Michael Potmesil and Indranil Chakravarty. lens and aperture camera model for synthetic image generation. ACM SIGGRAPH Computer Graphics, 15(3):297-305 1981.
Tim Seizinger, Florin-Alexandru Vasluianu, Marcos V Conde, and Radu Timofte. Bokehlicious: Photorealistic bokeh rendering with controllable apertures. arXiv preprint arXiv:2503. 16067, 2025.
Xiaoyong Shen, Aaron Herizmann, Jiaya Jia, Sylvain Paris, Brian Price, Eli Shechtman, and Ian Sachs. Automatic portrait segmentation for image stylization. In Computer Graphics Forum, volume 35, pp. 102. Wiley Online Library, 2016a.
Xiaoyong Shen, Xin Tao, Hongyun Gao, Chao Zhou, and Jiaya Jia. Deep automatic portrait matting. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part I 14, pp. 92-107. Springer, 2016b.
Yichen Sheng. Zixun Yu, Lu Ling, Zhiwen Cao, Xuaner Zhang, Xin Lu, Ke Xian, Haiting Lin, and Bedrich Benes. Dr. bokeh: differentiable occlusion-awan aware bokeh rendering. In Proceedings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 4515 2024.
Yujun Shi. Chuhui Xue, Jun Hao Liew, Jiachun Pan, Hanshu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 8839-8849, 2024.
Pratul P. Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, and Jonathan T. Barron. Aperture supervi- sion for monocular depth estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.
Zachary Teed and Jia Deng. Raft: Recurrent all all-pairs field transforms for optical flow. In Computer Vision-ECC 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pp. 402-419. Springer, 2020.
Yao Teng, Enze Xie, Yue Wu, Haoyu Han, Zhenguo Li, and Xihui Liu. Drag-a-video: Non-rigid video editing with point-based interaction. arXiv preprint arXiv:2312.02936 2023.
Neal Wadhwa, Rahul Garg, David E Jacobs, Bryan E Feldman, Nori Kanazawa, Robert Carroll, Yair Movshovitz-Attias, Jonathan T Barron, Yael Pritch, and Mare Levoy. Synthetic depth-of-field with single-camera mobile phone. ACM Transactions on Graphics (ToG), 37(4):1-13, 2018
Ting-Chun Wang, Ming-Yu Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro. Video-to-video synthesis. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 1152-1164, 2018.
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing. 13(4):600-61, 2004.
Weijia Wu, Zhuang Li, Yuchao Gu, Rui Zhao, Yefei He, David Junhao Zhang, Mike Zheng Shou, Yan Li, Tingting Gao, and Di Zhang. Draganything: Motion control for anything using entity representation. In European Conference on Computer Vision, pp. 331-348. Springer, 2024.
13

---

Published as a conference paper at ICLR 2026
Yang Yang, Haiting Lin, Zhan Yu, Sylvain Paris, and Jingyi Yu. Virtual dsir: High quality dynamic depth-of synthesis on mobile platforms. Electronic Imaging, 28:1-9, 2016.
Shengming Yin, Chenfei Wu, Jian Liang, Jie Shi, Houqiang Li, Gong Ming, and Nan Duan. Dragnuwa: Fine-grained control in video generation by integrating text, image, and trajectory. arXiv preprint wrXiv:2308.08089 2023.
Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, and Stanley Chan. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. CVPR, 2025.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018
Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, and Ren Ng. Synthetic defocus and look-ahead autofocus for casual videography. ACM Transactions on Graphics (TOG), 3864):1-16, 2019.
Rui Zhao, Yuchao Gu, Jay Zhangjie Wu, David Junhao Zhang, fei Liu, Weijia Wu, Jussi Keppo, and Mike Zheng Shou. Motiondirector: Motion customization of text-to-video diffusion models. In European Conference on Computer Vision, pp. 273-290. Springer, 2024.
Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint 1805v/80050960 2018.
Chengxuan Zhu, Qingnan Fan, Qi Zhang, Jinwei Chen, Huaqi Zhang, Chao Xu, and Boxin Shi. Bokehdiff: Neural lens blur with one-step diffusion. In IEEE International Conference on Computer Vision, 2025.
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A
APPENDIX
CONTENTS
A.I
Usage of LLM
16
A.2
The Details of Datasel
16
A.3
User Study Details
16
A.4
VEPT Metric Details
17
A.5
More Ablation Study
17
A.5.J
MPI Attention
17
A.5.2
Weighted Overlap Inference Strategy
17
A.6
More Visualization Results
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In the appendix, we begin with the LLM usage statement, followed by detailed description of our dataset, user study, and the VEPI metric. We then report additional ablation studies, covering MPI attention and the weighted overlap inference strategy. Finally, present additional visualization results to further demonstrate the superiority of our approach.
A.1 USAGE OF L.LM
In this study, a large language model (L..M) was used to polish the writing and elevate the overall textual quality.
A.2 THE DETAILS OF DATASET
objects from the video matting dataset (Lin al. 2021 and image matting dataset (Li et al. 2022 Hu et al. (024) and collect 1,300 background images from the intern dataset and background dataset (Lin al.) 2021). Following MPIB (Peng et al.) 2022b), we use a ray-tracing--ee based method to generate accurate bokeh effects. Specifically, we assume that the disparities of all images are planar, with their size and position randomly determined. The disparity map d is then set as a plane equation of pixel coordinates (z,y)
As mentioned Sec. 4.
ax by
(8)
where a, b, and parameters that define the spatial depth relationship between pixels. For each pixel, we sample multiple rays passing through the lens, find the intersection of each ray with the scene, and project this intersection onto the sensor plane obtain the final render results. As shown in Fig. 7 for each video, we randomly select background and foreground clips, as well as the focal plane and aperture. The foreground objects are moved along random 3D trajectories, with movement in six dimensions: forward and backward, left and right, up and down. Each video contains 25 frames, and all videos in the dataset have a resolution of 1024 576 pixels. To evaluate model performance, we use the same approach to synthesize a test set of 200 videos.
All in Pocus
Dispanty
Bokeh Resutts
Figure 7: Example of synthetic datasets, randomly define the focal plane, the position of each foreground, and the blur intensity.
A.3 USER STUDY DETAILS
Given the subjective nature of perceiving video bokeh rendering results, and the absence of ground truth (GT) data for video bokeh, we conducted a user study on real-world videos to evaluate different methods from subjective perspective. We randomly selected videos from the DAVIS dataset, each with resolution of 1024 x 576, featuring subjects such as people, animals, and various other objects. Since ground truth disparity maps were unavailable, we generated disparity maps using a depth prediction model (Chen al. 2025). Different methods were used to render the videos with the same control parameters. During testing, the videos were presented in random order to avoid bias. As shown in the interface (Fig. 8). participants were asked select the method that produced the
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Video1
Video2
Question:
Choose your favorite bokeh effect
O Videol
O Video
Same
Confirm
Figure 8: The user study interface, where they were asked select their preferred videos.
most consistent and aesthetically pleasing bokeh effect. If they found difficult to decide, they were allowed skip making selection.
A.4 VEPI METRIC DETAILS
We adapt the Edge Preservation Index (EPI) from prior image-based work (Joseph al. 2017). In VEPI, a Laplacian kernel is applied to generate binary edge maps for both the reference image and the bokeh-rendered image:
r(As - As, AS A
VEPI
(9)
T(As - As, As As) T(AS AS, AS -
where
82)
Σ s1(1,)) 82(1.))
(10)
SJEROI
ROI denotes the unblurred foreground region. As(i,) and As(i.) are the Laplacian-filet filtered versions the ROI in the reference sii and the bokeh rendering š(2, j). respectively. Since this metric relies precise foreground delineation, employ the DAVIS dataset (Perazzi et al. 2016) which provides densely annotated video segmentation. We compute VEPI for all videos in the DAVIS dataset, using the provided segmentation masks as the ROI, and report the mean value across all videos as the final result.
A.5 MORE ABLATION STUDY
A.5.1 MPI ATTENTION
Table 8: Ablation results for MPI attention. "MPI" refers to MPI attention, while "PR" indicates pretrained SVD weights.
Variants
MPI
PR
FD
RMJ
VFID-I
FVDJ
SSIM
PSNR
#1
0.551
0.013
4.521
21.941
0.905
31.936
#2
0.568
0.014
4.714
22.556
0.901
31.575
#3
0.586
0.014
4.537
27.743
0.893
30.988
To validate the role MPI Attention within the MPT spatial block, we report results two simple alternatives. First, we discard the guidance of the focal-plane-adapee attention with standard self-attention. As shown in the second row of Tab. [8] all metrics decrease, indicating that the sataal-ppae-aaapple MPI mask effectively provides geometry prior, resulting in temporally consistent and high-quality bokeh. Second, we remove the pretrained SVD weights and train the MPI spatial block from scratch. The results in the third of Tab. demonstrate that the pretrained 3D priors significantly improve model performance.
MPI mask and replace MPI
A.5.2 WEIGHTED OVERLAP INFERENCE STRATRGY
As introduced in Sec. 3.5 the weighted overlap inference strategy employs a weighting factor for blending overlapping frames. We evaluate two schemes: (i) cosine-hased blending, which emphasizes
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Imput
Output
Ingat
Output
Impot
Output
Figure 9: Visualization results on challenging real-worke scenes with fast motion, varying lighting. and occlusions.
central frames within each segment, and (ii) linear-based based blending, which assigns weights linearly as 7j z. As shown in Tab. 9 cosine-based blending provides superior temporal consistency and hugher-quality bokeh. Consequently, we adopt the bosine-based blending (Eq. (7) as the weighting factor for overlapping frame fusion.
Table 9: Results for different blending strategy.
Method
FD4
RM4
VFID-L,
FVDJ
SSIM
PSNR
Linear
0.422
0.007
1.565
9.168 9.005
0.972
38.706
Cosine
0.431
0.007
1.479
0.974
38,899
A.6 MORE VISUALIZATION RESULTS
Real-world scenes are often highly complex. To further demonstrate the effectiveness of our method such settings, we present rendering results on three challenging scenarios characterized by fast motion, varying lighting, and occlusions. As shown in Fig. 9 our method still produces high-quality renderings under these challenging scenarios.
More Videos Visualization:
We present further comparisons between Any-to-Ba Bokeh and baselines to validate its ability to render bokeh under poor depth conditions. In Fig. 10 and Fig. []] errors in depth prediction are visible along fine edges, such as hair, dog fur, or earrings. BokehMe, DeepLens, MPIB, and Dr. Bokeh rely heavily on accurate depth; in regions with missing erroneous depth, they fail to capture foreground edges, resulting in degraded bokeh. Although BokehDiff can render some edge textures, it struggles to remain consistent with the input. Our method effectively addresses these challenges, producing spatially accurate, depth-aware blur.
Visualization Results under Suboptimal Depth Inputs:
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Disparity
All-in- Focus
BokehDiift
BokehMe
Deeples ens
MPIB
Dr
Ows
Figure 10: Comparison results with baselines under suboptimal depth inputs. To highlight the differences, we zoom in on the red and green regions.
Real-World Videos Comparison: We further compare our model with baselines using two repre- sentative examples. As shown in Fig. 12 and Fig. [3] our method preserves the fine details of the in-focus subject, achieving more visually coherent bokeh. Even under challenging depth conditions, it accurately captures the edges of the subject, enhancing the bokeh realism. In contrast, BokehDiff struggles with the accurate rendering of subject textures, such as the characters on the hat (green box) and hair strands (red box). Furthermore, BokehMe, DeepLens, MPIB, and Br.Bo unable to preserve hair details within the same focal plane due to poor depth estimation.
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Disparity
All- Allin--Roos Focus
BokehDiff
BokchMe
DeepLers
MPIB
DrBokch
Ouns
Figure 11: Comparison results with baselines under suboptimal depth inputs. To highlight the differences, we zoom in on the red and green regions.
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Disparity
All-in-Focus
BokehDiff
BokehMe
DeepLens Lens
MPIB
Dr.okeh
Owis
Figure 12: Comparison results with baselines real-world videos. The area inside the red border is zoomed to highlight more details. Please zoom view them.
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Disparity
All-in-Focus
UAF
UAE
UAE
BokehDiff
DAE
DAE
NAEE
Coca-oola
BokehMe
DeepLens
MPIB
Dr.okehh
UAE
UAE
UAE
Ous
Figure 13: Comparison results with baselines on real-world videos. The area inside the red and green border is zoomed in to highlight more details. Please zoom in to view them.
22


Reviews:
[{"review_id": "vk0UAoyHBa", "reviewer": "Reviewer_Y63a", "summary": "This paper introduces a novel one-step diffusion framework for generating temporally coherent and depth-aware video bokeh effects. The proposed approach enables video refocusing with explicit control over the focal plane and bokeh intensity, addressing the limitations of prior image-based and video-based bokeh rendering methods. The framework leverages a focal-plane-adapted multi-plane image (MPI) representation to guide the diffusion process, ensuring temporal smoothness and accurate depth-dependent blur transitions.", "questions": "See weaknesses above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Innovative Approach:**\n   The use of an MPI-guided conditioning mechanism in a one-step diffusion framework for video bokeh generation is both original and well-motivated. It effectively bridges the gap between static image refocusing and temporally coherent video refocusing.\n\n2. **Temporal Coherence and Depth Awareness:**\n   The focal-plane-adapted MPI representation efficiently balances detail preservation in focused regions and smooth transitions in defocused areas, improving visual consistency across frames.\n\n3. **Comprehensive Experiments:**\n   The paper includes thorough quantitative and qualitative comparisons with existing methods, demonstrating consistent improvements in temporal stability, spatial accuracy, and controllability.", "weaknesses": "1. **Dependence on Depth Estimation:**\n   The method relies on pre-trained depth estimation models as input. In dynamic or complex scenes, depth errors may propagate into the final bokeh rendering. The paper would be stronger with a sensitivity analysis or ablation showing how depth inaccuracies affect output quality.\n\n2. **Computational Efficiency:**\n   While the results are impressive, the paper provides limited discussion on computational cost. Diffusion-based models are typically resource-intensive; more details on runtime, memory consumption, and scalability (e.g., potential for real-time use) would enhance the practical relevance.\n\n3. **Dataset Bias and Generalization:**\n   The primary evaluation uses synthetic datasets. Although this allows for controlled comparisons, such datasets may not fully reflect real-world complexities such as fast motion, varying lighting, or occlusions. Additional experiments on diverse real-world datasets would strengthen claims of robustness and generalizability.", "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": "SudpaIq5c9", "reviewer": "Reviewer_7dEg", "summary": "The authors propose a method for adding controllable Bokeh in videos. They fine tune a video diffusion model to accept a video and a corresponding explicit scene geometry conditioning and output a depth-aware Bokeh video in a single diffusion step. The authors propose a multi-stage training strategy that facilitates robustness to noisy input geometry and high temporal bokeh consistency. The authors provide extensive evaluations showing state-of-the-art results for controllable video bokeh, with control of the focus plane and bokeh intensity.", "questions": "Please see weaknesses section.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1.\tThe authors leverage the prior of a video diffusion model in a novel way, to perform temporally consistent, controllable video bokeh. \n2.\tThe authors showcase good bokeh results. They also provide a supplementary video with their results and comparisons to other methods, which is very important for the qualitative assessment of their claims for temporally consistent bokeh addition.\n3.\tThe authors show extensive quantitative evaluations, emphasizing their lead over other competing methods.\n4.\tThe authors provide several ablations for their training strategy choices.", "weaknesses": "Major:\n1.\tThe authors do not provide limitations for their method. Are there any scenarios where the model fails to generate a good bokeh video? Maybe in videos with fast motion, such as a car race.\nMinor:\n1.\tFigure 1 is not referenced.\n2.\tSM figures 10,11 – red border not corresponding to zoom-in area.", "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": "f66UEwKWn7", "reviewer": "Reviewer_cW3t", "summary": "In this paper, the authors present a video bokeh rendering method based on diffusion model. They propose a one-step diffusion framework, and employ a multi-plane image representation to the focal plane as a condition. Also, they introduce a progressive training strategy for stability. Experiments on synthetic and real-world benchmarks show the performance of their methods.", "questions": "- Weighted overlap inference strategy. I wonder if doing weighted overlap only during inference without the same operation during training will affect inference performance.\n- The number of image plane N. I want to know whether to use the same N or different N for different scenarios. If different, how is the value of N determined.\n- Additional metrics. Add LPIPS for frame-level perceptual quality;  \n- Dynamic effects. I suggest the author provide video effects", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The authors propose a one-step diffusion framework for video bokeh rendering, which exhibits an efficiency advantage in inference time.\n- In the third stage, fine tune the VAE decoder and introduce texture loss based on image gradients to improve high-frequency texture and edge clarity, which helps ensure the presentation of details of the focused subject.\n- Time consistency and video quality indicators are significantly better than the baseline mentioned in the paper.", "weaknesses": "- Comparison with Video Bokeh Methods. The authors only compared their with the image bokeh method, but it needs to be compared with video methods, such as VBR [1].\n- Complex scenes. The authors employ a multi-plane image (MPI) representation, and this representation can bring challenges, such as whether to divide an object into two different layers. The authors should discuss this situation.\n- The robustness of this method. The authors should compare their full model with degraded depth maps and without degraded depth maps.\n- Missing details. TS (token selection) is not defined in formula (4); How M̄=[1,M] aligns a “global token” with 2D masks should be provided; Specify mask resolutions for each block and the exact interpolation strategy; clarify how “near-focus vs. wide-interval” masks are selected per layer.\n- Dataset and generalization. Both synthetic training and testing are based on the construction of \"planar disparity\" (where d is an affine function of x and y). This simplifies geometry but differs significantly from real-world scenarios.\n\n[1] Luo, Yawen, et al. \"Video bokeh rendering: Make casual videography cinematic.\" Proceedings of the 32nd ACM International Conference on Multimedia. 2024.", "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)