Machine Unlearning in 3D Generation: A Perspective-Coherent Acceleration Framework
Authors: Shixuan Wang
Jingwen Ye
Xinchao Wang
Venue: NeurIPS 2025 poster ()
Keywords: Machine Unlearning
3D Tasks
Perspective Acceleration
Multi-View Efficiency
Primary area: social_and_economic_aspects_of_machine_learning

Abstract: Recent advances in generative models trained on large-scale datasets have enabled high-quality 3D synthesis across various domains. However, these models also raise critical privacy concerns. Unlike 2D image synthesis, where risks typically involve the leakage of visual features or identifiable patterns, 3D generation introduces additional challenges, as reconstructed shapes, textures, and spatial structures may inadvertently expose proprietary designs, biometric data, or other sensitive geometric information.
This paper presents the first exploration of machine unlearning in 3D generation tasks. We investigate different unlearning objectives, including re-targeting and partial unlearning, and propose a novel framework that does not require full supervision of the unlearning target. To enable a more efficient unlearning process, we introduce a skip-acceleration mechanism, which leverages the similarity between multi-view generated images to bypass redundant computations. By establishing coherence across viewpoints during acceleration, our framework not only reduces computation but also enhances unlearning effectiveness, outperforming the non-accelerated baseline in both accuracy and efficiency.
We conduct extensive experiments on the typical 3D generation models (Zero123 and Zero123XL), demonstrating that our approach achieves a 30\% speedup, while effectively unlearning target concepts without compromising generation quality. Our framework provides a scalable and practical solution for privacy-preserving 3D generation, ensuring responsible AI deployment in real-world applications. The code is available at: https://github.com/sxxsxw/Fast-3D-Unlearn-with-Skip-acceleration

Machine Unlearning in 3D Generation:
A Perspective-Coheren Acceleration Framework
Shixuan Wang Jingwen Ye" Xinchao Wang"
National University of Singapore e13528540u, nus edu, (jingweny, xinchao) enu..edu.ssssee..ee
Abstract
Recent advances in generative models trained on large-scale datasets have enabled high-quality 3D synthesis across various domains. However, these models also raise critical privacy concerns. Unlike 2D image synthesis, where risks typically involve the leakage of visual features or identifiable patterns, 3D generation intro- duces additional challenges, as reconstructed shapes, textures, and spatial structures may inadvertently expose proprietary designs, biometric data, other sensitive geometric information. This paper presents the first exploration of machine un- learning in 3D generation tasks. We investigate different unlearning objectives, including re-targeting and partial unlearning, and propose a novel framework that does require full supervision of the unlearning target. To enable more efficient unlearning process, we introduce a skip-acceleration mechanism, which leverages the similarity between multi -iew generated images to bypass redundant com- putations. By establishing coherence across viewpoints during acceleration, our framework not only reduces computation but also enhances unlearning effective- ness, outperforming the non-accelerated baseline in both accuracy and efficiency. We conduct extensive experiments on the typical 3D generation models (Zero and 7erol 13311,, demonstrating that our approach achieves 30% speedup, while effectively unlearning target concepts without compromising generation quality. Our framework provides scalable and practical solution for privacy-preserve 3D generation, ensuring responsible Al deployment real-world applications. The code is available at: http://gihbb.com//xxxx///B Uww.----nnnna-h----eee acceleration
1 Introduction
The ability to generate realistic and diverse 3D content is crucial for applications in gaming, film production, virtual reality, and digital design, where high-quality 3D assets are high demand. To address this need, 3D generation models Waag et al. (2025), Nash et al. (2020), Raj et al. [2023] have become key research focus in computer vision and graphics, aiming automate the creation of detailed and structured 3D representations.
Despite significant advancemenss, 3D generation also introduces pressing privacy concerns. Many models, particularly aarge-scale 3D generative foundation models Liu et al. 2023a], (Tang et al. [024). trained on extensive datasets, increasing the risk of incorporating proprietary, sensitive, personally identifiable information. This can lead to potential data leakage or unauthorized content reproduction. Additionally, generative 3D models may inadvertently expose intricate details of objects, raising ethical and legal challenges. Addressing these privacy risks is critical for the responsible development and deployment of 3D generation technologies, yet remains underexplored issue.
*Corresponding authors
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Source
Partial Unlearn
Input
Image- to-3D
Stylization
Retarget
Figure 1: Given a single input image, the target Image-to-3D model generates multi-view repre- sentation of the object. Our proposed framework applies an unlearning process to the target model, enabling tasks such as stylization removal, partial unlearning, and retargeting.
Over the past few years, the community has increasingly recognized the importance of ensuring trust and safety in modern generative models. In particular, there is growing interest in developing efficient unlearning methods to remove private or sensitive information from trained models. Given the high cost of retraining large-scale generative models, machine unlearning aims to selectively erase the influence of specific data without requiring full model retraining. Recently, Seo et al. d.o et al.) (2024] introduced GUIDE, framework designed prevent the reconstruction of specific identity by unlearning the generator using only a single image. Their approach demonstrates the effectiveness of generative machine unlearning, highlighting the feasibility of targeted knowledge removal.
Despite recent advancements machine unlearning for image generation, little to no work has explored its application in 3D generation. We argue that trust and safety concerns in 3D generation are just as critical, yet they introduce unique challenges compared to 2D generation. First, since 3D generation involves multi-view image outputs, correcting modifying specific targets requires consistency across all viewpoints, significantly increasing annotation complexity. Second, unlearning must be applied each generated view, making the process computationally expensive and time- consuming. These challenges highlight the need for efficient and scalable unlearning techniques tailored 3D generation.
In this work, we propose the first 3D unlearning framework, targeting zero-shot image-to-31 view synthesis models. These large-scale models, trained on vast datasets, pose increased privacy risks. Our goal is to unlearn specific object while preserving the generation performance for other objects. To achieve this, we leverage the inherent similarity between different viewpoints, reconstructing unseen views using only the nearest few. Additionally, we introduce an efficient caching mechanism for the diffusion process of key views, significantly accelerating the denoising process for the input object. The unlearning tasks in 3D include changing the style, retargeting to a completely new object, or partially editing the given object. We demonstrate several cases solved our framework in Fig.
conclusion, machine unlearning 3D tasks, our contributions could be concluded as:
First, we pioneer the exploration of unlearning in Image-to-3D models, addressing the removal entire objects, specific views, and the style of 3D objects.
Second, we propose an accelerated unlearning process for image-to-3D models, demonstrat- ing that full supervision with all target object views is unnecessary, making our approach more practical for real-world applications.
Lastly, conduct various unlearning experiments in 3D tasks, and methods maintain the generative quality while achieving 30% speedup.
2 Related Work
2.1 3D Generative Models and Acceleration Techniques
In the past few years, 3D generation has gained significant attention, whose methods including point clouds Bello et al. 22200, Wu et al. [2019, Achlioptas al. (2018), voxels Liu et al. [2200] Ren et al. [024]. meshes sallicooooou et al. [2024], Guédon and Lepetit 2024 Wu et al. 2024a and implicit fields Sun et al. 20244] Deng et al. [2021) However, these methods often lack generalization,
they typically designed for generating specific categories. To overcome this limitation, many

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researchers have focused on large-scale 3D generation frameworks trained on extensive 3D datasets. Specifically, line of research aims to directly learn single-shot novel view generation models conditioned on camera viewpoints from large-scale 3D datasets. For example, Zero 23 Liu et al. [2023a] is proposed as a framework for changing the camera viewpoint of an object given just a single RGB image. Following this work, Objaverse-XI. (Deike et al. [2023) utilizes over 100 million multiview rendered images for training, thus achieving strong zero-shot generalization abilities. Magic 123 Qian et al. [2023] is presented as a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generations with both 2D and 3D priors.
Generative tasks often rely on diffusion models, which involve computationally intensive sampling processes. As a result, recent research has focused on accelerating the generation process |Ma et al.
2024),Huang et al.] [2025], Yao et al. [2025]. [So et al. [2023]. For example, Ma et al. MM et al. [2024] propose the DeepCache framework as a novel training-
paradigm to accelerate diffusion models
from the perspective of model architecture. And S*-DMs Wang and Li] (2024) utilizes the accelerating
mechanism to reintegrate the information omitted during the selective sampling phase. This challenge even more pronounced in 3D generation, where optimization typically requires tens of thousands of
iterations of full-image volume rendering and prior model inferences, often taking tens of minutes per
shape. To improve efficiency, numerous studies Liu et al. 2023b] [Shi et al. [2023] Liu et al. [2023c),
Li al. 2023], Liu et al. 2024a] have explored ways to accelerate both training and reconstruction. For instance, One-2-3 3-5 Liu et al. [2023b] utilizes multi-view images predicted by Zero123 to generate a textured 3D mesh in just 45 seconds, while One-2-3-45+4 Liu et al. (2023c] enhances
texture quality through lightweight optimization. In contrast to these pecelrration-oouseee methods, our framework is designed for unlearning, aiming to efficiently remove the influence of specific views
or concepts.
2.2 Machine Unlearning
The concept of machine unlearning is firstly introduced by Bourtoule et al. |Bourooule et al. [202]] ), which aims to eliminate the effect of data point(s) on the already trained model without retraining the model from scratch, In the past few years, it has been well studied especially in classification tasks Tarun et al. [2023], Ye et al. [2022]. Kurmanji et al. [2023). However, these approaches face scalability challenges in generative tasks due to the massive training datasets and the large model sizes involved.
Since large-scale models are trained on extensive datasets, they often raise privacy concerns, prompt- ing increasing research efforts address these issues Liu al. [2025], (hh et al. [2024), J. Li et al.)
2025). Liu et al. [. For instance, Liu et al Liu et al. [025 investigate machine unlearning in
large language models (L.Mss), aiming to remove sensitive or illegal information while preserving essential knowledge and model capabilities. In diffusson models, Wu et al [Wu et al. (202bb) propose aligning the output domains of sensitive and anchor concepts through adversarial training, while meta-unlearning Gao et al. [2024) not only removes harmful or copyrighted concepts but also pre- vents their malicious relearning. Additionally, Score Forgetting Distillation (SFD) Chen et al. [2024) accelerates forgetting while preserving generation quality and improving inference speed. Our work further enriches this field by being the first to explore unlearning in 3D generation, extending the scope of machine unlearning to address ethical concerns in generative Al.
3 Methods
3.1 Problem Formulation
Our target model is a zero-shot image-to-3D view synthesis model f, which generates multi-view 3D) representations from a single image. Recall that in the 2D setting, unlearning aims to remove specific objects or attributes from a pre-trained generative model while preserving its ability to generate other realistic images. Given diffusion- based generative model f. which generates images from noise 2, such unlearning modifies the model to ensure that target object I, is removed while maintaining overall generation quality:
= fu,z (). S.t. ā a L,
(I)
where fa represents the unlearned model for the target object. The main challenge in 2D unlearning lies in selectively forgetting the exact object without affecting unrelated generations. Since image
3

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synthesis occurs in a single 2D space, this process is computationally feasible using methods such as gradient-based fine-tuning or regularization- -aasdd memory erasure.
3D generation models, such as zero-shot image-to-3i synthesis models f. are typically trained to learn mapping from single input image / to set of novel viewpoints for 3D reconstruction:
X (=(0)
2: (0) f(I,)))
(2)
where z=() denotes the synthesized image from viewpoint 8. These models often trained on large-scale datasets and may inadvertently memorize information from the training data, leading to potential privacy risks.
Unlike 2D unlearning, where modifications are applied to a single image, 3D unlearning must ensure that the target object is removed across multiple viewpoints. This requires updating the model across the full set of angles + leading to significantly higher computational cost:
uullcam
- OE
Σ
where i((f) is the target image with the sensitive content removed at viewpoint 6. Since diffusion- based 3D models generate each view iteratively, this increases the overall training cost by a factor of [0), where [+] is the number of sampled viewpoints.
Our goal is develop an efficient 3D unlearning framework that removes specific objects attributes (donated as the forget set D2) from the target model f while preserving its ability to generate accurate 3D) views of other objects (donated the preservation set D,). To achieve this, propose dynamic skipping scheme (Sec. 3.2p that accelerates the 3D unlearning process by strategically leveraging multi-view consistency, reducing redundant computations while maintaining coherence across viewpoints. Throughout the rest of the paper, we use the re-targeting task as an illustrative example of our approach. Specifically, we aim to adapt the generation results for the forget set D, so that they resemble those of designated re-target set Do. This objective is formalized as:
( E ~ (f(1,) Do}, {fu(I,0) E D,} ~ (f(1,0) I Dr). (4)
where fu denotes the updated model after unlearning, and the goal is to make the outputs of fu on D indistinguishable from those D.-
3.2 Dynamic Skipping via Interpolation
To address the high computational cost of 3D unlearning across dense viewpoints, we introduce dynamic skipping scheme. Instead of independently unlearning each view, our method strategically selects sparse set of key viewpoints and interpolates the remaining ones. By leveraging multi-view consistency, this approach significantly reduces redundant updates while preserving visual coherence across views.
For each selected key view O, the reverse diffusion process is performed iteratively over T steps. At each step, the model refines the latent representation by removing a portion of the noise, gradually approaching the clean image. The denoising process is defined as:
أं + f(z;), T,T I,...,
(5)
At the final step, = denotes the fully denoised sample, corresponding the final synthesized image for view 0z,
To further optimize the denoising process, we introduce interpolation- acceleration technique that eliminates redundant computation across views. The core idea is to cache intermediate diffusion states from a small set of reference viewpoints, denoted as 0, E O,. For each reference view 0,, we pre-compute and store the entire reverse diffusion trajectory:
Cache + {(x,()))) | 0, E O,}. where |0,1 << |0].
(6)
In the following part of the paper, we simplify x. (0,) as of, where the superscript denotes the reference viewpoint corresponding to 0,.
This cache in Eq. 6 plays central role in perspective-aware accellrratiioooo framework by:

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Accelerating Inference: Providing cached diffusion states as reference anchors to efficiently initialize and interpolate intermediate views, reducing redundant computation.
Enhancing Generation Quality: Serving geometric prior to ensure coherence across neighboring viewpoints, which in turn improves the quality of multi-view image synthesis.
Once the reference trajectories are stored, we accelerate the denoising for each sample angle O, by interpolating between the states of the two closest reference viewpoints, θ,, and f,2, based on their similarity to f.. Specifically, we select the closest reference viewpoints R₂ {θr, fr, } as the two reference angles that maximize the similarity measure S(0,, 0,):
Rg arg max {S((., 0,)). 0/2 E R.
6,
We compute SO,,, 0,) using CLIP--baed similarity by incorporating viewpoint information directly into the CLIP input. Specifically, we define:
S(O,, 0,) = cos (CLIP(I, 02), CLIP(I,
(8)
where CLIP 8) denotes the CLIP embedding obtained by feeding the image along with viewpoint 0 as imput (as joint representation). The angular difference between 0g and the reference angles determines whether to skip intermediate timesteps.
The angular difference between 0, and the selected reference angles determines whether to skip intermediate timesteps the denoising trajectory. Specifically, we compute:
A0 min { 0r,l, 10, -0-21
Given empirically validated threshold 20 (as shown in the supplementary), we dynamically adjust the timestep 'jump from which denoising begins:
lupper: A0 T
'jump
(10)
flower:
otherwise.
We empirically set fupper and flower to control the degree of skipping, corresponding to aggressive and conservative denoising strategies, respectively. Then, after determining the timestep fjump. the initial state for denoising at angle 9g then interpolated from the cached reference states timestep T /jump'
(r1)
T-uuo = Wrr IT Thapp
+ Wra
.TT Ijaup"
(11)
where the interpolation weights Wris Wr2 are computed from the normalized similarity scores defined in Eq. g
S(0., Or.) Wy. S(O., 0,,) + S(0,,0,,))
ie [1,2].
(12)
This strategy provides efficient initialization and ensures geometric consistency by starting the denoising process closer to convergence and reducing redundant computation across similar views.
Finally, starting from the interpolated state, we perform the remaining denoising steps from t T fjump to t = 1:
(1) = (x) L= l.umps.... 1,
(13)
yielding the final denoised output (0)) This scheme effectively reduces redundant computation while preserving the fidelity of multi-view 3D representations.
3.3 Accelerated Unlearning with Remain and Forget Losses
The dynamic skipping scheme enables efficient computation across all viewpoints, which could be leveraged to update the target fu. as achieve unlearning on the unlearn set D).-
For conducting unlearning on the forget set D,, we firs train fake score network Sy for the guidance on updating f.. And during the training of Sy. the target model f keeps fixed. To be concrete, Sy is initialized by the pre- pre-trained score network S.. The training of the Fake Score Network involves two key loss functions:
Lin Lin remain + p Lia forget
(14)
5

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Fake Score Remain Loss: This loss is used to train the Fake Score Network to replicate the noise prediction of the pretrained model-true score on the remaining samples. This ensures that when the Generator generates images for the remain set its conditional score aligns with the pretrained model, maintaining the original generation quality.
in remain Dr. (101)) +0,((( -cl
(15)
where ~ (O, 1) is the noise perturbation.
Fake Score Forget Loss: This loss is used to train the Fake Score Network to output noise prediction different from the original class for unlearn class samples, tending towards the distribution of the override image. In our retarget task, we align the noise prediction with that in D.. By altering the noise prediction, the Generator is indirectly guided to "forget" the features of the target class.
+ - Lin forget = Ex,~D,, Xow ((0.) - + 01] (16)
After training the fake score network Sy, we use it to guide the unlearning of the target model f. resulting in the updated model fu. This process aims to balance two objectives: (1) retaining the generation quality on the remain set D,, and (2) suppressing the model's capacity to reconstruct the forget set D.-
The loss used to update f is defined as:
Lival X, Li remain Ay-
Leg forget:
(17)
where Ay and ' control the trade-off' between remain and forget objectives. And at this stage, the fake score network Sy keeps fixed. And the two loss items are defined as:
Diffusion Remain Loss: We preserve generation quality for the remain set by encouraging the updated model fu to generate outputs whose score under Sy matches the ground-truth noise:
Eg remain Ex,ED,,esN(011 [f +c,
(18)
This loss ensures that generation quality on the remain set is degraded after unlearning.
For the forget set, guide the model move away from its original generation path, and instead produce outputs whose score under Sy aligns with that of the override distribution Do:
Diffusion Forget Loss:
Lg forget
en.(..) [1s) -, Sy((()))) +e, +)1
(19)
This loss prevents the model from reconstructing features related to the forget set and enforces retargeting.
By jointly optimizing these loss functions, we ensure that the diffusion model gradually unlearns the forget set while preserving its generation quality on the remain set. This process is repeated iteratively. with the model being updated using gradients derived from both loss terms. Additionally, acceleration techniques, such as the dynamic skipping scheme, can be incorporated to improve efficiency and stabilize training dynamics. These techniques enable the model to reach an effective unlearning state with fewer iterations, thereby reducing computational costs while maintaining performance.
4 Experiments
We have performed various unlearning tasks and presented their evaluation results, with additional implementation details provided in the supplementary material.
4.1
Experimental Setting
Datasets. We conduct experiments on three types of data: (1) Ten 3D Minions models collected from the internet (denoted as Minl0), with one used for training and the rest for testing; (2) Rendered 3D

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Table Quantitative comparison the quality of synthesized novel views against ground truth views
under different reference angles and step skip selections.
Method
Steps Skipped
Training Time
SSIM
LPIPS 4
APSNR (dB)
Baseline Non--ccceeeaated
226.5
0.766
0.160
0.00
3 Ref Angles
196.2 (1.13)
0.760
0.159
+0,295
12
180.0 (1.23)
0.752
0.182
-0.605
16
170.1 (1.30)
0.762
0.151
+0.338
4 Ref Angles
196.2 (1.13)
0.770
0.148
+0.418
12
180.9 (1.22)
0.783
0.136
+1.093
16
167.4 (1.32)
0.761
0.155
+0.163
8 Ref Angles
8
193.5 (1.14)
0.746
0.171
-0,446
12
180.9 (1.22)
0.752
0.161
-0.093
171.0 (1.30)
0.761
0.161
0.000
objects from Objaverse 1.0, including sculptures, traffic barriers. and fire hydrants; and (3) subset of five Objaverse models rendered from 24 viewpoints, each with 35 images, totaling approximately 4,200 ground-truth images.
We evaluate our approach across three key aspects: generation quality and efficiency, effectiveness of unlearning, and preservation of retained knowledge. The following metrics are used. (1) SSIM: Structural Similarity Index, which measures the similarity between generated images and their ground truth. Higher values indicate better structural preservation. (2) LPIPS: Learned Perceptual Image Patch Similarity, a metric that quantifies perceptual differences. Lower values are preferred. (3) APSNR: The difference in Peak Signal-to-Noiss Ratio between the generated images and ground truth images. (4) AFID: The change in Fréchet Inception Distance (FID). which measures the difference between the feature distributions of real images and generated images, A lower AFID indicates that the generated images have become closer to the real images in terms of perceptual quality, while higher AFID suggests greater divergence between the generated and real image distributions. (5) Inference Time (Speedup Analysis): Measures the computational efficiency of the proposed accelerated unlearn method.
Evaluation Metrics.
4.2 Experimental Results
Quantitative comparison of the proposed framework with discrete reference angles and step skips. We evaluate our accelerated unlearning framework on the Min10 dataset by comparing against a baseline (non-accelerated) approach. In this experiment, we set the total number of view- points to [0] 40, where the baseline performs unlearning across all angles exhaustively. Our method, in contrast, leverages view coherence to infer fewer reference angles while maintaining or even improving unlearning performance. To analyze the effects of our dynamic skipping mechanism, we conduct ablation studies by varying the number of reference angles and controlling the step skip- ping range, with fjower = fupper ensure fixed skip lengths. We evaluate both image quality using SSIM, LPIPS, and APSNR and training efficiency, summarized in Table
As shown in Table I our accelerated unlearning framework significantly reduces training time, with up to 1.32 times speedup compared to the baseline. Importantly, this efficiency gain does not come at the cost of image quality. The setting with 4 reference angles and 12 steps skipped achieves the best overall performance, improving SSIM by 0.017, reducing LPIPS by 0.024, and increasing APSNR by 1.093 dB relative the bascline. These results demonstrate that modeling cross-view coherence not only enables faster training but also leads to more effective unlcarning, outperforming the baseline that processes all viewpoints exhaustively, And the visual comparison results are depicted in Fig 2 which further illustrates how different skip step settings affect the visual quality of synthesized views.
Performance other unlearning tasks.
In addition to retargeting task, we also conducted experiments on other types of unlearning tasks. Specifically, we selected ten categories and performed unlearning each category individually. For each task, the current category was treated as the forget set, while the remaining nine categories formed the remain set. In total, we conducted ten unlearning tasks. For each task, compared
7

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Sumple
Sample
Steps Skipped
12 Steps
Skippal
Sleps Stipped
Figure 2: Visual comparison of generated novel views under different skip step settings. The results demonstrate how varying the number skipped steps affects synthesis quality.
Table 2:
This table presents comparison of SSIM and LPIPS metrics hetween the synthesized novel view images and their corresponding ground truth images at various angles for different unlearn tasks when the panget_image is unlearned. We calculate the metric both the forget set and the remain set.
Forget Set
Remain Set
Unlearn Task
Model
SSIM t
LPIPS
SSIM
LPIPS J
Original
0.802
0.250
0.783
0.286
Yellow Car Transformation
Unlearned
0.898
0.066
0.781
0.336
Original
0.752
0.345
0.789
0.276
Metal Syle Icecream Transformation
Unlearned
0.829
0.102
0.797
0.315
Original 0.790
0.270
0.785
0.284
Bronze Statue Transformation
Unlcarned
0.819
0.142
0.793
0.308
Original 0.770
0.315
0.787
0.279
Cherry to Banana
Unlcarned
0.829
0.140
0.792
0.257
Original
0.810
0.230
0.783
0.288
Barrier to Fire Hydrant
Unlearned
0.843
0.117
0.744
0.262
Original
0.761
0.330
0.788
0.277
Football to Phone
Unlearned
0.698
0.301
0.744
0.328
Original
0.785
0.285
0.785
0.282
Barrel Add Black Lid
Unlearned
0.722
0.142
0.736
0.291
Original
0.807
0.240
0.783
0.287
Doraemon with Hat
Unlearned
0.744
0.139
0.749
0.297
Original
0.797
0.260
0.784
0.285
Minion With Backpack
Unlearned
0.789
0.139
0.783
0.252
Original 0.779
0.300
0.786
0.281
Stool with Pot
Unlearned
0.846
0.192
0.795
0.271
the image generation quality of both the forget set and the remain set before and after unlearning. Detailed unlearning targets provided in the supplementary material, and quantitative results reported in Table
From the results in Table 2 the SSIM and LPIPS metrics for both the forget set and remain set across ten unlearning tasks are reported. After unlearning, the SSIM scores the forget set generally increase, while the LPIPS scores significantly decrease, indicating that the forget set has been effectively altered and no longer faithfully reconstructed seeeuutteddrelletinngn reflecting successful unlearning. For instance, in the 'Yellow Car Transformation task', the forget SSIM improves from 0.802 to 0.898, and LPIPS drops from 0.250 to 0.066. Similar trends are observed in most tasks, such as 'Metal Style Ice Cream Transformation' and 'Cherry to Banana'.
Meanwhile, the performance on the remain set remains relatively stable, with only minor variations in SSIM and LPIPS. This suggests that the unlearning process selectively affects the forget set

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Unkearing
Figure 3: Visualization of generated results for different unlearning tasks. Each row corresponds to one unlearning task, where the diagonal entries represent the forget set.
without significantly compromising the model's ability to generate high-quality results for the remain set. These findings demonstrate that our method achieves targeted unlearning while preserving generalization performance.
We further visualize the generation results in Fig. where each row corresponds to specific unlearning task. The diagonal entries show the generated results for the forget set, which have been mapped to their respective unlearn targets. The off- off-diagonal entries correspond to the remain set. As illustrated, the forget set images on the diagonal exhibit clear shift toward the designated unlearn targets, indicating successful forgetting. Meanwhile, the generation quality for the remain set remains consistent, demonstrating that our approach effectively removes the targeted information without significantly affecting unrelated content.
Conclusions
The rapid advancements in generative models trained on large-scale datasets have enabled the synthe- sis of high-quality 3D samples across diverse domains. However, these developments also introduce critical privacy concerns. This paper presents the first exploration of machine unlearning in 3D generative models, addressing the unique challenges posed by multi-view consistency and spatial dependencies. We propose novel approach that exploits the inherent similarities between images rendered from different perspectives to introduce a skip acceleration mechanism. By strategically by- passing redundant computations, our method enhances efficiency while preserving task performance, providing a promising direction for privacy-award 3D generation. In the future, we plan to extend our research to other image-to-3D generative models, further exploring unlearning techniques tailored to different architectures and training paradigms.

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Acknowledgement
This project is supported by the Ministry of Education, Singapore, under its Academic Research Fund Tier 2 (Award Number: MOE-T2EP20122-0006)
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Appendix
Supplementary Methods
6.1 Framework Algorithms
We propose a novel two-stage framework that integrates dynamic timestep skipping with directional unlearning, enabling efficient and precise removal of targeted concepts from a diffusion- -ased generative model. This section provides supplementary algorithms for our proposed framework, including Algorithm @aaihmmmannn and Algorithm
Algorithm Dynamic Skipping via Interpolation
Require: Total timesteps T. base angles 0g. all the sample angles 0, each sample angle 8,, weight factor Wy, noise perturbation e e~ (O, 1), angle threshold len, interpolation upper time step tupper. interpolation lower time step flower, and noise weight factor ew. xx(0) represents the denoised result at timestep & during the diffusion process, conditioned on the angle 0. At timestep
0, (0) the final denoised image, and timestep T, xrr(() (0) is the noisy image or latent representation.
Examples base angles:
2: 3 base angles: Os {0 - ]
3: base angles: O2 = {0P: 90" -90 180%
4: 8 base angles: O,- = {0", 9,,, 135",
133", 180 1 135", -90,, -45%
5: for each sample angle 0g do
6: Compute CLIP similarity S(O,, 06) with key angles
7: Select the two most similar key angles as 0,, 0g
8:
Determine skip steps based threshold and similarity Interpolate xz (0,) from Ie (6)) and xi (82) using:
9:
10:
xe (0,) Arz + (1 (82) 800_.0 .1)
11:
A 310. 8, (0,,,
12:
if 00 0, < Oe. then
Use interpolated He I. T hupper
14: else
15:
Use interpolated 2, at t T tower
16: end if
17:
end for
This algorithm accelerates the diffusion process by dynamically skipping denoising steps through interpolation. For each sample-conditioned angle 9,, it identifies the two most similar base angles 0, and 0₂ using a similarity metric (e.g.. CLIP similarity). Then, it interpolates the intermediate denoised result r=(0)) from the known results at 0 and 0g via weighted average governed by their similarity scores:
Description of Algorithm I
S(B,. 01) (0.) = (0x( + (02); S(0,, 01) + S(G,, 0z)
Depending on whether sample angle is sufficiently close to base angle (determined by a threshold 0.)), the algorithm either uses the interpolated result at a higher or lower timestep (i.e., fewer or more skipped steps). This allows the system to trade off between fidelity and speed while maintaining semantic consistency.
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Algorithm 2 Unlearning via Dynamic Acceleration with Remain and Forget Losses
Require:
Pre-trained score network S,, unlearned model for the target object fa, fake score network
Sy. remain set D,. unlearn set D,, override set D., all the sample angles each sample angle
8g, batch size B, weights À > 0, 1 0
Initialize S and fu from pre-trained model
2: for each epoch do
3: Sample batch X, D,. X, Dj, X, ~ Do
4: Call Algorithm 1 with 0,
> Interpolation Acceleration
5:
for each sample angle 8, in do
Train Fake Score Network
6:
Compute Lin remain (0.)
7:
Compute Lin forget (0.)
8:
Compute total loss: Lyn = XC remain + pC fn forget
9:
Update Sy using gradient descent on Lnn X, p. weights for remain/forget tasks
I> Train Generator
10:
Compute Lg remain (0.)
11:
Compute Ly Jonges (0.)
12:
Compute total loss: Lg Xg remain J&l forget
13:
Update fa using gradient descent on Lg
X, weights for remain/forget tasks
end for
15: end for
This algorithm presents training framework for concept unlearning by alternately optimizing the generator and a fake score network using supervision from the remain, forget, and override datasets. A key innovation of this framework lies the of dynamic skipping (realized by Algorithm]] D to accelerate the diffusion process for arbitrary sample angles, enabling efficient training while preserving semantic consistency in generated outputs.
Description of Algorithm 2
At the beginning of each epoch, Algorithm | is invoked perform interpolation sampling across all base angles. This preprocessing step prepares the interpolated denoising results, allowing for fast inference at any sample angle 0g by reusing the precomputed intermediate states.
Subsequently, the algorithm iterates through all sample angles 0, defined in the training setup. For each 0,, alternates between training the fake score network and the generator. The score network is updated using remain and forget losses to reflect the desired unlearning behavior, while the generator optimized using the same objectives remove target concepts while preserving unrelated features.
By integrating dynamic acceleration and angle-wise alternating optimization, this framework achieves fine-grained control over the forgetting process in diffusion models, while significantly reducing the computational burden of full denoising for every training step.
6.2 Dynamic Acceleration Threshold Selection Basis
Recall that in the main paper (see Eq. (10)), empirically set the angular threshold 20 to guide the dynamic adjustment the denoising timestep fjump- Below, we provide supplementary justification for this choice.
Specifically, we precompute and cache intermediate denoising results for a discrete set of reference viewpoints across all sampling steps. For each non-reference training view, we identify its nearest reference angle via cosine similarity and interpolate the cached features at matched time steps to approximate the denoising trajectory. This enables skip-sampling mechanism in which certain sampling steps are bypassed by reusing spatially coherent representations.
Motivated by the inherent geometric consistency among nearby viewpoints, we hypothesize that smaller angular distances to reference views indicate higher structural similarity and, consequently, greater tolerance for step skipping. Based on this observation, we design a dynamic skipping scheme where the number of skipped steps is conditioned on the angular proximity the nearest reference angle. In later experiments, quantitatively assess the trade-off between generation quality and sampling efficiency under this dynamic scheme using SSIM, LPIIS, and APSNR, as well as overall training speedup.
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6.2.1 Marginal Benefit Analysis
We introduce the concept of marginal benefit as a key indicator for dynamic acceleration threshold selection. Combining SSIM decrease and LPIPS increase into single quality loss metric:
QQtotal = a AQSSIM + 3. ALLPIPS
AS
Seurent Sprevious
(20)
Marginal Benefit
AQuota
(Q previous Qeument) + 3 (Leurrent Ipprevous))
Objective:
Find the threshold range that maximizes marginal benefit, i.e., achieve the greatest acceleration improvement with the minimal quality degradation.
Weight coefficients and need to be adjusted based on business requirements (defaulting to 0.5 each).
Physical meaning:
AQSSIM = Q previoos Qcarrent (SSIM decrease, larger value means more quality loss).
ALLPIPS Leurrent L Lerevious (LPIPS increase, larger value means more percep- tual difference).
AS Seurrent Sprevious (Speed-up Ratio increase, larger value means faster reasoning).
6.2.2 Experimental Setup and Threshold Determination
To determine the optimal dynamic acceleration threshold, sampled 36 viewpoints within the [0 ]5% range from the base view at 2 intervals, using reference views. Experiments were con- ducted on the Yellow Car unlearning task. For each candidate threshold, computed SSIM, LPIPS, and acceleration ratio under the dynamic 4-view, 12-step sampling configuration, and subsequently calculated the marginal benefit. The angle yielding the highest marginal benefit selected the optimal dynamic threshold. As shown Figure 4a the marginal benefit peaks at a threshold of 20".
To further validate the effectiveness of the proposed dynamic strategy, compared it with static configuration using 4 reference views and uniform steps, without threshold adaptation. This comparison demonstrates that our strategy can achieve acceleration while maintaining high generation quality. The results of this comparative experiment are presented in Figure 4b
From the figure, we observed that as the angular threshold increases from 0' to 45%, the SSIM decreases from 0.69 0.51, while LPIPS increases from 0.05 0.36, indicating a consistent trade- off between fidelity and efficiency. Meanwhile, the acceleration ratio improves from the static baseline 0.29 up 0.48. Notably, the 20" threshold yields balanced performance achieving 0.62 SSIM, 0.22 LPIPS, and 0.38 acceleration ratio and represents the optimal marginal gain point. Beyond 20", marginal returns diminish: from 20 30", acceleration increases only 0.06, while LPIPS worsens 0.07. After 30", visual degradation accelerates, with LPIPS exceeding 0.3 and SSIM dropping below 0.6. These results confirm that moderate thresholds (approximately 20") achieve the best trade-off, while aggressive skipping leads to diminishing quality returns.
22

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Vergral Eenalt Trreaald
and fase Hesica Threarodd
1.09
0.3
030
6.5
for fie Ball loue
4.50
Thrcsted
Thrsllda
(a) Marginal benefit vs. threshold angle.
(b) Comparison between dynamic and static strategies.
Figure 4: Analysis of threshold selection and strategy comparison.
7 Supplementary Experimental Results
7.1 Experimental Settings
Implementation Details
To cover the full 360 horizontal field of view, we define the angular range as (-180", 180"] Given the desired number of reference directions Niefrrncce we generate set of reference angles starting from 0 with fixed interval of 360" /Nieference- As these angles are initially defined in the [0", 360%) range, we map them into 18000 180%] to align with the defined coordinate system.
During the training stage of the unlearning task, we additionally sample one angle every 10 over the range from - 1880 to 180%, excluding the reference angles. This ensures dense and uniform coverage across the entire horizontal span. Such setup helps the model generalize to diverse viewpoints while maintaining consistency between the training and evaluation angular distributions.
Hyper-parameter Settings
In all experiments, we employ the Adam optimizer, where 31 and B3 denote the exponential decay rates for the first and second moment estimates, respectively. The parameters À and μ represent the regularization coefficients used in the objective function. The term denotes the standard Gaussian noise added during the diffusion process time step f, with 6, NN(0 1).
The Number of References represents the number of pre-eached reference angles used for subsequent interpolation to estimate noise; the Skip Steps indicates the initial steps skipped during the sampling process.
Table 3: Hyperparameters for Fake Score and Generator
Parameter
Fake Score
Generator
1.0
1.0
0.01
0.01
Optimizer
Adam
Adam
Learning Rate
4 10-6
1
81
0.0
0.0
82
0.999
0.999
e
10-8
10-8
23

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Table 4: Experimental Settings for Different Reference Angles and Unlearn Effects
Parameter
Reference Angles Experiment
Target Forget Images Experiment
GPU
NVIDIA A A10 80GB
NVIDIA A6000 48GB
Batch Size
8
2
Sample Steps
32
32
Training Epochs
5
Number of References
Skip Steps
12
7.2 Multi-angle presentation of the results from the main experiment
We provide additional experimental results supplement the main paper. The following provides concrete examples of the unlearning implementation for the retargeting, stylization, and partial tasks in our experiments.
Table 5: Representative application cases categorized by type.
Category
Case Examples
Style Transfer
Yellow Car Transformation, Metal Style Ice-cream Trans- formation, Bronze Statue Transformation Cherry Banana, Barrier Fire Hydrant, Football to Phone
Whole Object Retarget
Partial Edit Replacement
Barrel Add Black Lid, Doraemon with Hat, Minion with Backpack, Stool with Pot
Unlearning task 1: Style Transfer
Yellow Car Transformation: In this experimental setup, frontal image of silver car is designated as the forget image, representing the category to be unlearned, while a frontal image of a yellow car which generated by adjusting the color changing the car body color to yellow while keeping other visual content unchanged, serves the override image, representing the target category. During training, the forget image combined with a given sample angle is replaced by the override image with the same corresponding sample angle. This configuration aims to evaluate the model's ability to forget and override when the object's appearance attributes, such color, change.
of the original image,
Metal Style Ice-cream Transformation: The forget image a Green ice cream cone, while the override image is generated by changing the color of the ice cream to a metallic sheen. Similar to the Yellow Car Transformation case, both the forget angle and the override angle are aligned with the sample angle.
Bronze Statue Transformation: The forget image is white marble sculpture, while the override image generated by changing the color of the sculpture bronze, Again, both the forget angle and the override angle are aligned with the sample angle.
Unlearning task 2: Whole Object Retarget
In this experimental setup, a frontal image cherry is designated as the forget image, representing the category to be unlearned, while frontal image of banana serves as the override image, representing the target category. During training, forget image combined with given sample angle replaced by the override image with the same corresponding sample angle. This configuration is designed to evaluate the model's capability in performing semantic transformation between different object categories.
Cherry to Banana:
The forget image is a barrier, while the override image is fire hydrant. Similar to the Cherry to Banana case, both the forget angle and override angle are aligned with the sample angle.
Barrier to Fire Hydrant:
Football to Phone: The forget image is a football, while the override image phone. Again, both the forget angle and override angle aligned with the sample angle.

---

Unlearning task 3: Partial Edit Replacement
Minion With Backpack This setting aims to evaluate the model's response to viewpoint variations and additional attribute modifications. The forget image is frontal view of minion. When the sample angle lies within the range [-90", ) the override image is the same as the forget image, and both the forget angle and override angle match the sample angle. However, when the sample angle falls outside this range (i.e., side or rear views), the override image is replaced by rear view of the minion wearing a red backpack, and the override angle is defined as the sample angle plus 180 (i.e., the opposite viewing direction). This setting simulates the unlearning and rewriting behavior when the target object undergoes structural or appearance changes under different viewpoints. The specific angle relationships are as follows:
If the original forget angle is within [-909, (-90 the guidance condition uses the original forget image and forget angle.
If the forget angle lies in [-180", -0%), the guidance condition replaces the image with the override image and adjusts the angle to forget angle plus 180".
If the forget angle lies in (90", 180%) the guidance condition replaces the image with the override image and adjusts the angle to forget angle minus 180%
Barrel Add Black Lid: generated by adding big black lid to the original barrel. Both the forget angle and the override angle are aligned with the sample angle.
The forget image a wooden barrel, while the override image is
Doraemon With Hat:
The forget image is Doraemon, while the override image is generated by putting a red cap on Doraemon's 's head.. Both the forget angle and the override angle aligned with the sample angle.
Stool With Pot:
The forget image is a wooden stool, while the override image is generated by placing small plant in a pot the stool. Again, both the forget angle and override angle are aligned with the sample angle.
Figure 5 presents multi-view visualizations of the unlearning outcomes for various target objects across multiple categories, demonstrating the consistency and robustness of the unlearning effect under different viewing angles.
In each row, the left-most pair shows the original source object (left) and the desired unlearned target (right). The right panel visualizes the unlcarned results rendered from multiple canonical perspectives (front, side, back, etc.). It can be observed that across diverse object includin vehicles, statues, characters, and everyday items-the model consistently applies unlcarning effects to generate novel outputs aligned with the desired target identity or semantics. For instance, the "car" reliably altered to resemble a yellow sports model across all views, while the "Doraemon" character consistently altered to wear red hat across all viewpoints, suggesting strong disentanglement and generalization capacity in the forgetting process.
These results validate that our method does not overfit to single viewpoint, but achieves semantically coherent forgetting across multiple 3DDconsistent renderings, highlighting the model's capacity for multi-perspecte semantic consistency in unlearning tasks.
25

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Source Image
Target Image
Different Angles
Figure 5: Demonstration of multi-persspecivv effects on the forget set for different unlearning tasks.
7.3 3D Reconstruction Demonstrations
7.3.1 Qualitative Results 3D Unlearning
We showcase 3D reconstruction results for several tasks, presenting pairs of rendered images and depth maps.Thess results demonstrate that our unlearning strategy not only performs effectively in multi-view consistency settings but also extends to full 3D geometry. Specifically, we observe consistent suppression of undesired concepts across different viewpoints and depth cues, indicating that unlearning has been successfully integrated into the volumetric representation. This highlights the generalizability and spatial coherence of our method beyond view-hased supervision, ensuring that undesired features removed holistically rather than superficially.
26

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Pink Minien Transformation
Unicurned Model
Daraemen With Hat
Bundionn
Berrier To Fire Hydrant
Unlearned Medel
Figure 6: Qualitative 3D reconstruction results across different tasks after unlearning.
60
5
X
100
100
Figure 7: more angles sampled in 3D rec
7.3.2 Results on Free3D Framework
To demonstrate the generalizability our dynamic skipping framework beyond Zero 23, we integrate it with Free3D, a state-of-the-ar diffusion-based 3D generation model. Our method is adapted by aligning the multi-view conditioning and applying the view-consistent acceleration strategy during the denoising process.
27

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input
0%
45
90"
135
180
Figure 8: Multi-view 3D generation results using Free3D integrated with our dynamic skipping framework. Leftmost column: Input view. Remaining columns: Generated novel views from different angles. The high consistency and quality across views demonstrate the effectiveness and generalizability of our method on the Free3D architecture.
28

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Figure 8 shows multi-view renderings of 3D objects generated by Free3D enhanced with our dynamic skipping approach. Each row presents different object reconstructed from single input view (shown on the left), with subsequent columns showing synthesized views from novel angles. The results exhibit high visual fidelity, geometric consistency, and smooth transitions across viewpoints, confirming that our acceleration framework is effective in improving inference efficiency while preserving generation quality on diverse 3D diffusion architectures.
This successful integration underscores the flexibility and broad applicability of our method, position- ing it as a promising general acceleration paradigm for multi-view 3D generation systems.
Effect of View-consistent Acceleration without Unlearning
To isolate the effect of acceleration from unlearning, we conduct an ablation study on the Zero 123 baseline by applying our multi-view consistency-guidee acceleration without any unlearning objective. Specifically, we introduce skip-step sampling with different reference view counts (3/4/8 views) to observe how generation quality changes purely due to acceleration.
Table reports the AFID scores, computed as the difference between the FID of accelerated models and the baseline Zero-I-to- model. Positive values indicate improved fidelity relative to the baseline, while negative values indicate degradation.
Table 6: A FID Comparison between Accelerated Models and Baseline (Zero-l-to-3)
Method
Steps Skipped
Delta FID
8
+0.9779
View
12
-6.7059
16
-15.8247
8
+0.3379
View
12
-7.1140
-34.8952 16
8
+2.8421
View
12
-10,5640
16
-74.9905
Table 7: Generation Quality Metrics under Different Dynamic Skipping and Reference Views Configurations on 123.
Metrics
SSIM
LPIPS
PSNR
MSE
zero123
0.7469
0.2526
13.53
0.0597
3view 8skip
0.7683
0.2417
14.29
0.0494
3view 12skip
0.7641
0.2463
14.17
0.0507
3view 16skip
0.7605
0.2532
14.04
0.0532
4view 8skip
0.7728
0.2389
14.34
0.0424
4view 12skip
0.7701
0.2418
14.27
0.0441
4view 16skip
0.7674
0.2485
14.13
0.0511
Sview 8skip
0.7672
0.2331
14.57
0.0467
Sview 12skip
0.7643
0,2338
14.51
0.0483
Sview 16skip
0.7614
0.2515
14.41
0.0487
29

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Ground Truth
Object
3ref
3ref
3ref
4ref
4ref
4ref
Bref
Bref 12skip
Sref
Zeco123
8skip
12skip
16skip
8skip
12skip
16skip
8skip
16skip
120"
Banana
Hat
Mog
Speaker
Telephone
Figure 9: Qualitative results demonstrating the effect of view-consistent acceleration without unleam- ing. (Corresponds to Table data)
We analyze the results from two perspectives: the number of reference (baseline) views used for interpolation, and the number of diffusion steps skipped during inference.
30

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Effect of Reference View Number: When fixing the number of skipped steps, increasing the number of reference views generally leads to more accurate initialization for the diffusion process due to finer angular coverage and closer interpolation points. This advantage is reflected in the 8-step skip setting, where the model with reference views achieves the highest positive AFID (+2.8421), compared to 3 and 4 views (+0.9779 and +0.3379 respectively). This suggests that denser set of baseline views provides better starting point, facilitating high-fidelity multi-view synthesis.
Effect of Skipped Difffusion Steps: Across all reference view counts, increasing the number of skipped diffusion steps significantly degrades performance. For example, under 8 reference views, the Delta FID drops from +2.8421 1 at 8 skipped steps to 10.56400 at 12 skipped steps and further to 774.9905 16 skipped steps. This trend indicates that while skipping steps can accelerate inference, excessive step skipping undermines the model's 's ability to refine the initial interpolated latent, leading to poorer image quality.
Interaction between Reference Views and Skipped Steps: Interestingly, the degradation caused by skipping more steps is more pronounced as the number reference views increases. This is likely because the interpolation between two nearby reference views produces a finer but potentially more complex latent initialization that requires sufficient diffusion steps to properly refine. When too many steps are skipped, the model lacks the capacity to adequately recover details and enforce multi-view consistency, resulting in a sharper performance drop.
Qualitative results illustrating these effects are shown in Figure 9
7.5 Inference Efficiency
the baseline represents the time (1.1000 seconds) taken by Zero123 to sample an image without using dynamic skipping via interpolation. The other columns show the sampling times with different skip steps, along with the speedup ratios compared to the baseline.
As shown in Table g
Table 8: Inference Time and Speedup of Dynamic Skipping Strategies.
Method
Full Sample (s)
Skip 8 Sample (s) Skip 12 Sample (s) Skip 16 Sample (s)
Baseline (No Accelerate)
1.1000
Accelerated (Skip 8)
1.1000
0.7709
Accelerated (Skip 12)
1.1000
0.6459
Accelerated (Skip 16)
1.1000
0.5193
Speedup (vs. Baseline)
1.4286
1.7072
2. 221210
The speedup ratios are calculated as Speedup = Baellie Time
Accelergted Time" Results show that skipping 8, 12, and 16 steps achieves 1.43x, 1.71 x, and 2. x2x faster inference, respectively, demonstrating the efficiency of our dynamic skipping strategy.
8 Limitation and Social Impact
While method introduces a novel framework for machine unlearning in 3D generation, several limitations remain. We categorize these into technical limitations and broader societal concerns, and outline promising future directions address them.
Technical Linitations.
Model Generalization. Our framework is currently validated on Zero123 and Zerol 23XL. Its applicability to other 3D generation paradigms (e.g., NeRFs, mesh-based models, point- based representations) remains untested and may require architectural adaptations.
View Similarity Estimation. The dynamic skipping mechanism leverages CLIP-based sim- ilarity to approximate view-level correspondence. While practical, this may be suboptimal for objects with subtle geometric or structural variations that CLIP embeddings cannot fully capture.
31

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Manual Target Selection. The forget/remain/retange sets are manually specified. Real- world deployment would benefit from automatic identification of privacy-sensitive or biased content, requiring new detection or attribution tools.
Hyperparameter Sensitivity. Our method depends on empirically chosen parameters, such the angular threshold т and skip-step schedule. These may require retuning datasets or under different acceleration regimes.
Lack of Robustness Evaluation. We do not assess the robustness of the unlearned model against adversarial attacks such as model inversion, concept re- re-injection, prompt- based data recovery.
Future Work.
Broader Model Applicability. We aim to adapt our framework to a wider range of 3D gen- eration backbones, including volumetric NeRFs, implicit surfaces, and real-time rendering architectures.
Privacy-Aware Target Detection. Future work will explore integrating privacy or attribution detectors to automatically identify sensitive content for targeted unlearning without human intervention.
Unlearning Without Retargeting. While our method currently aligns forgotten content with retargeted distribution, we plan to investigate pure erasure techniques without replacement, suitable for content removal rather than transformation.
Online and Continual Unlearning. Extending our method dynamic settings-such as continual learning or post-deployment unlearning requests- is an important direction for practical applications.
Trustworthy Unlearning Evaluation. We plan to develop formal verification protocols and benchmarks to quantify the effectiveness and irreversibility of unlearning across diverse tasks and threat models.
Social Impact Considerations.
Our framework raises potential concerns regarding privacy leakage and model bias, especially in the context of modular or pre-trained model reuse.
Privacy Risk. By reusing pretrained parameters, the unlearned model may unintentionally retain latent traces of upstream data. Adversaries could potentially reconstruct sensitive content through model inversion prompt tuning. One mitigation strategy is increase the diversity and number of pretrained models used, ensuring that no single model contains sufficient information to recover sensitive content.
Model Bias. Biases present in the original training data source models may propagate through the unlearning process. To mitigate this, we propose diversifying the source model pool and introducing prrmmotty regularization during training. This helps prevent over-reliance on any single biased component and encourages fairer predictions.
We consider these directions essential for improving the robustness, faireess, and ethical deployment of 3D unlearning systems, and plan to extend our study to address these limitations in future iterations of this research.
32


Reviews:
[{"review_id": "h1V71bJAcG", "reviewer": "Reviewer_sLqD", "summary": "This paper presents the study of machine unlearning in 3D generative models. The authors propose a unlearning framework that supports both re-targeting and partial unlearning without requiring full supervision of the unlearning targets. A key contribution is the introduction of a skip-acceleration mechanism that leverages viewpoint similarity to reduce redundant computation across multi-view generations. This not only improves computational efficiency but also enhances unlearning effectiveness.", "questions": "I am not vary familiar with the field of machine unlearning, and I look forward to the authors’ clarifications and responses to the weaknesses.", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Machine unlearning in 3D generation is a timely and important topic that remains underexplored. With the rapid advancement of 3D foundation models and the increasing utilization of 3D data, protecting 3D intellectual property is becoming a critical challenge for the community. Therefore, investigating machine unlearning in 3D generation is both necessary and meaningful.\n\n2. The authors successfully extend the SFD framework from 2D generation to 3D generation and demonstrate its effectiveness to a certain extent.\n\n3. The authors propose an acceleration strategy specifically designed for multi-view generation, which significantly improves unlearning efficiency (as shown in Table 1).\n\nWeaknesses:\n1. Lack of Validation on Unseen Data. The paper lacks evaluation on an independent validation set. It is recommended that the authors include an additional unseen set (e.g., GPTEval3D) beyond the defined remain $D_r$, $D_o$, $D_f$ sets to assess whether the model’s general generation capabilities are adversely affected.\n\n2. Corruption in Figure 3. In Figure 3, the Minion character appears corrupted across all settings. It would be helpful if the authors could explain the cause of this behavior and discuss potential solutions or limitations.\n\n3. Generality of the Method. The generalization ability of the proposed method remains unclear. It would strengthen the paper to include experiments on unlearning a broader set of images from the same class (e.g., more instances of Doraemon) to assess class-level forgetting performance.", "was_revised": true, "final_justification": "Thanks for the authors’ response. For W2 and W3, the absence of visual results makes it challenging for me to assess their effectiveness. I understand that this is partly due to rebuttal constraints and am inclined to trust the authors’ explanation.\n\nI hope the authors will develop a more comprehensive benchmark by incorporating: 1) a larger validation set to safeguard base model performance; 2) a wider range of category tests to ensure generalizability. Lastly, I appreciate the contributions of this paper as an early effort in Machine Unlearning for 3D Generation and will maintain my positive score.", "rebuttal": "We sincerely appreciate your positive feedback and recognition of our work. Your acknowledgment of the importance of machine unlearning in 3D generation is truly encouraging, and we are grateful that you highlighted the timeliness and relevance of this research. We also value your comments on our extension of the SFD framework to 3D generation and the proposed acceleration strategy for multi-view generation. Your feedback reinforces the significance of our approach, and we are glad that it resonates with you.\n\n---\n\n\n\n**W1: Lack of Validation on Unseen Data. The paper lacks evaluation on an independent validation set. It is recommended that the authors include an additional unseen set (e.g., GPTEval3D) beyond the defined remain $D_r$, $D_o$, $D_f$ sets to assess whether the model’s general generation capabilities are adversely affected.**  \n\nThank you for your suggestion. The evaluation on an independent validation set is given in the main paper and supplementary. Below, we give additional details on the quantitative results in **Table 2** and the qualitative results in **Figure 3**. \n\n- First, it is important to clarify that the purpose of $D_r$ - remain set is to ensure that, during the unlearning process, we not only forget the features of the \"forget image\" class but also ensure that the generation of other classes is not affected. The **remain set** dataset comes from Shapenet's rendered results, covering multiple categories and different angles of images.\n\n- In **Figure 3**, all input images are rendered from Objaverse 1.0, and the **remain set** does not contain any images rendered from Objaverse 1.0. Taking the **Cherry to Banana** task as an example, $D_f$ is a photo of a cherry, $D_o$ is a photo of a banana, and **remain set** consists of images from Shapenet. Therefore, for the **Cherry to Banana** unlearning task, all input images except for \"cherry\" (such as car, chair, sculpture, etc.) belong to the \"unseen set.\"\n\n- Additionally, we appologize for some ambiguity in the presentation of **Table 2**, which may cause misunderstanding. We would like to clarify that the data in the **remain set** is used to evaluate the model's ability to maintain the **unseen set**. For the **Cherry to Banana** task, the **remain set** row in **Table 2** shows the following results (the original one is tested on Zero123):\n  \n|Task|Unlearned SSIM|Unlearned LPIPS|Original SSIM |Original LPIPS |\n|-|-|-|-|-|\n|Cherry to Banana|0.792|0.257|0.787|0.279|\n\nIn the table, these values were obtained by inputting the images from the other 9 unlearning tasks (car, chair, sculpture, etc.) into the unlearned model (the cherry-to-banana model), and comparing the generated images with the ground truth for these input images (car, chair, sculpture, etc.). In this task, compared to directly inputting the unseen set (car, chair, sculpture, etc.) into the original model (Zero123), which resulted in **SSIM = 0.787** and **LPIPS = 0.279**, the values show minimal change. This demonstrates our success in maintaining the **unseen set** effect.\n\n---\n\n**W2: Corruption in Figure 3. In Figure 3, the Minion character appears corrupted across all settings. It would be helpful if the authors could explain the cause of this behavior and discuss potential solutions or limitations.**\n\nThank you for pointing this out. This issue stems from the experimental setup. We have since re-adjusted the parameters for generation to ensure consistent lighting and shading on the back of the Minion character. In our experiments, the randomness of the generative model can sometimes lead to inconsistencies in details, particularly in terms of lighting and shape. We will upload the modified generation results shortly and will continue to explore ways to further reduce randomness in the generation process to ensure high-quality, stable outputs.\n\n---\n\n**W3: Generality of the Method. The generalization ability of the proposed method remains unclear. It would strengthen the paper to include experiments on unlearning a broader set of images from the same class (e.g., more instances of Doraemon) to assess class-level forgetting performance.**\n\nThank you for your suggestion. We have indeed conducted experiments to demonstrate the generality of our method. However, due to space constraints in the main text, we have presented only a subset of the results. Here are the additional details:\n\nFigure 2 demonstrates the unlearning process applied to the front-facing Minion image from Figure 3, with the goal of transforming it into a pink suspenders style. Due to oversight, we did not provide additional explanation regarding this generality. In fact, we inputted five other Minion images with different body types and facial expressions, and the results showed that these Minions also successfully transformed into the desired pink suspenders style, with the suspenders, eyes, and other parts turning pink. This demonstrates the effectiveness of our method in handling different input images from the same class.\n\nAdditionally, for the ninth experiment in **Figure 3** (Minion with Backpack), we conducted multiple tests by inputting Minion images with varying numbers of eyes, body types, and facial expressions. The results showed that all of these inputs successfully added a red backpack to the Minion. Due to oversight, this part of the visualization was not included in the supplementary materials. We plan to upload the results of the **Minion with Backpack** task and the remaining images of the pink Minion transformation with different Minion inputs in the future, or we will supplement these results in the revised version to further support the generality and effectiveness of our method.\n\nFurthermore, we will also add additional visual results for other unlearning tasks to better demonstrate that our framework can achieve forgetting for a given class of images using just a forget image and an override image.\n\n---"}, {"review_id": "peOySReE6S", "reviewer": "Reviewer_TywL", "summary": "This paper tackles the task of machine unlearning for 3D asset generation. Specifically, this paper focuses on generative novel view synthesis given an input image, i.e., Zero123 and Zero123XL. To accelerate the unlearning, the authors propose to utilize the similarity between multiview images to cache and reuse the diffusion trajectories. Experiments on three self-developed datasets verify the effectiveness of the proposed approach.", "questions": "## 1. About the problem setup\n\nIt would be great if the authors can clarify the problem setup. For example,\n1. Is unlearning conducted case by case? Namely, will a separate unlearning be needed for each 3D generation?\n2. For an unlearning, how many \"target\" images will be given? Based on Eq. (3), it seems like mulitview images need to be provided?\n3. L237 mentioned `We conduct experiments on three types of data` but where are the results for the 2nd dataset:\n> (L238) Rendered 3D objects from Objaverse 1.0, including sculptures, traffic barriers, and fire hydrants\n4. I am quite confused about the 3rd dataset:\n> (L329) A subset of five Objaverse models rendered from 24 viewpoints, each with 35 images, totaling approximately 4,200 ground-truth images.\n\nCan the authors clarify the difference between `24` and `35`? Should 24 viewpoints mean 24 images?\n\n5. In Tab. 1, does `X Ref Angles` refer to the given \"target\" images? If so, why is the performance of `8 Ref Images`, i.e., most reference images, the worst? Does this mean that the proposed approach is ineffective in unlearning?\n\n## 2. About `dynamic skipping via interpolation` (Sec. 3.2)\n\nThe idea that exploits the similarity of nearest neighbour multiview images is interesting. However, I am not convinced by its fidelity, as no experiments are conducted to directly assess that. Since this is essentially the main contribution of the paper (L61), I think it should be more carefully studied.\n\nCan authors provide **both quantitative and qualitative** results for the following experiments (no learning needed):\n1. Render a 3D asset with $N_1 + N_2$ views\n2. Use those $N_1$ views as the reference images\n3. Run the \"dynamic skipping via interpolation\" for those $N_2$ views and compute metrics for the quality, e.g., PSNR, SSIM, and LPIPS, when compared to the ground-truth rendered ones\n\nThe authors can ablate different camera distributions for $N_1$ and $N_2$ to understand the limitation.", "limitations": "See \"questions\" above.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "1. Quality-wise: the qualitative results in the paper look promising;\n2. Clarity-wise: the paper is mostly clear;\n3. Significance-wise: the paper tackles an important task of 3D generation unlearning, which is important for privacy and large-scale usage;\n4. Originality-wise: the proposed idea is interesting and the results look reasonable.", "was_revised": true, "final_justification": "After reading the other reviews and the authors' rebuttal, my concern about the major technique is mitigated. However, the current presentation, e.g., problem setup and approach (e.g., how to obtain multiview), is quite confusing, which requires significant effort in polishing. Thus, I decided to raise my original score but keep it borderline.", "rebuttal": "Thank you for your constructive suggestions and recognition of our work! We have carefully considered your advice and would like to provide the following clarifications.\n\n---\n### **About the problem setup**\n**Q1. Is unlearning conducted case by case? Namely, will a separate unlearning be needed for each 3D generation?**\n\n**A1:**\nYes, the unlearning process for each class needs to be handled individually, but in practice, a single representative image from the class can serve as the \"representative\" for the entire class.\n\nIf a front-facing Minion image in a standing pose is used as the **forget image** and a corresponding pink-stylized Minion as the **override image**, the model will transform any Minion, regardless of variations (e.g., eye count, height, body shape), into the pink style, thus achieving machine unlearning.\n\nAs shown in **Fig.2** of the main text, unlearning the model with the front-facing standing Minion (from **Fig.3**) as the forget image and the pink Minion as the override image allows different Minion variations to transform into the pink style using this unlearned model.\n\nUnfortunately, we didn’t explicitly mention the generality of this approach. We tested five additional Minion images with varying features, and all successfully transformed into the expected pink style, with overalls and eyes turning pink. This demonstrates the method's effectiveness across different input images of the same class. We will provide supplementary visual results in future submissions.\n\n**Q2: For an unlearning, how many \"target\" images will be given? Based on Eq. (3), it seems like mulitview images need to be provided?**\n\n**A2:**\nThis part may not be clear enough. In fact, during unlearning, we require one \"forget image,\" one \"override image\" (i.e., \"target\"), and multiple sampled angles. Since the zero123 model can generate synthesized images from a single image across multiple viewpoints, although we only have one target image, we actually obtain as many target images as the number of sampled angles.\n\nAs mentioned in Question a, unlearning can be achieved with just one **override image** (i.e., the target) and one **forget image**. The **forget image** represents the class that we want the model to \"forget,\" while the **override image** (target image) represents the class that the forget image will eventually be transformed into.\n\nThe **remain image** is primarily used to prevent the model from becoming overly similar to the **target image**, so as not to affect the generation of other non-target images. The dataset for the **remain set** comes from Shapenet's rendered results, which include images from multiple categories and different viewpoints.\n\n**Q3: L237 mentioned `We conduct experiments on three types of data` but where are the results for the 2nd dataset:**\n\n> (L238) Rendered 3D objects from Objaverse 1.0, including sculptures, traffic barriers, and fire hydrants\n\n**A3:**\nThe experimental results involving the rendered 3D objects from Objaverse 1.0 (including sculptures, traffic barriers, and fire hydrants) are qualitatively demonstrated in Fig. 3 and quantitatively analyzed in Table 2.\n\nThis dataset serves two primary purposes in our experiments:\n\n1. Acting as the forget images for 10 unlearning tasks\n\n2. Comparing the performance of Zero123 unlearning with and without *dynamic skipping via interpolation*, as measured by SSIM and LPIPS on both forget and remain sets.\n\nTaking the *yellow car transformation* task (the first task in **Table 2** and **Fig. 3**) as an example:\n\n- The **forget set** metrics evaluate the unlearning effectiveness by comparing the output (from original car images fed into the unlearned model) against yellow car ground truth using SSIM and LPIPS.\n\n- The **remain set** metrics assess model preservation capability by averaging SSIM and LPIPS scores between outputs (from the other 9 categories' input images) and their respective ground truths.\n\nThis experimental design demonstrates that our approach successfully achieves target unlearning while maintaining generation quality on unseen data.\n\n**Q4: I am quite confused about the 3rd dataset:**\n> (L329) A subset of five Objaverse models rendered from 24 viewpoints, each with 35 images, totaling approximately 4,200 ground-truth images.\n\n**A4:**\nWe would like to clarify the following:\n\n- We selected five 3D models from the Objaverse dataset and rendered each from 24 viewpoints, spaced 15° apart, covering a full 360° rotation. Each viewpoint generated 35 images, totaling ~4,200 ground-truth images. Apologies for the previous confusion.\n\n- The relevant results are in **Supplementary Materials** (L184, Sec. 2.4, *Effect of View-consistent Acceleration without Unlearning*), where we validate dynamic skipping via interpolation. This method decouples acceleration from unlearning, showing that even without unlearning, generation time can be reduced and image quality improved.\n\n- A comparative analysis with varying reference viewpoints and skip steps shows that our framework produces higher-quality, more efficient images than Zero123. Qualitative results are in **Appendix Figure 4**, and quantitative results in **Appendix Table 4**.\n\nWe hope this clarification resolves any misunderstanding. Thank you for your understanding and valuable feedback.\n\n**Q5: In Tab. 1, does 'X Ref Angles' refer to the given \"target\" images? If so, why is the performance of '8 Ref Images', i.e., most reference images, the worst? Does this mean that the proposed approach is ineffective in unlearning?**\n\n**A5:**\nYes, 'X Ref Angles' refer to the given \"target\" images. And we found that using 4 reference angles yields better results than using 8. This phenomenon can be explained from the following perspectives:\n\n- **Authenticity of Reference Angles.**\nThe 8 reference images are synthesized from a single view using Zero123, not real multi-view ground truth. Since Zero123 may generate inaccuracies for distant viewpoints, these extra reference angles could introduce noise rather than useful information.\n\n- **Stability of Local Viewpoints.**\nWith 4 reference angles, adjacent reference views are 90° apart, allowing reliable interpolation by combining information from both angles during inference. With 8 reference angles, adjacent views are only 45° apart, leading to smaller geometric differences and redundant information, which complicates optimization.\n\n- **Rationality of Supervision Signal and Overfitting Risk.**\n As mentioned in L61, **full multi-view supervision is unnecessary**, as the model should perform inference rather than rigid fitting.\nFour reference angles provide sufficient geometric priors, whereas more closely spaced reference angles may cause the model to overfit to the distribution of synthetic images (e.g., texture details) while neglecting critical inter-view geometric relationships, ultimately degrading reconstruction quality.\n\nIn conclusion, **a smaller number of highly relevant reference angles (4) is more effective than a larger number of noisy viewpoints (8)**.\n\n---\n### **About `dynamic skipping via interpolation` (Sec. 3.2)**\n\n**Q1: Can authors provide both quantitative and qualitative results for the following experiments (no learning needed)**\n\n**A1:**\nYes, we have already conducted experiments to assess the fidelity of the proposed dynamic skipping,\nand the details are provided below:\n\n- Table 1 shows static skipping results using the Min10 dataset, which includes multi-view images of 10 Minion shapes. A front-view **forget image** was paired with a corresponding pink-styled **override image**. The setup includes nine configurations with 3, 4, and 8 reference angles and step sizes of 8, 12, and 16, plus one baseline (non-accelerated), all trained for 8 epochs. Training time per epoch and the acceleration ratio relative to the baseline are provided.\n\n- Due to an oversight, we did not provide a detailed explanation of the $t_{upper}$ and $t_{lower}$ values in equation (10). When calculating the data presented in Table 1, we set $t_{upper} = t_{lower}$, and both were set to the number of steps skipped in Table 1. Based on the image quality of the unlearned results (calculated by comparing to the ground truth using metrics such as SSIM, LPIPS, and delta PSNR), we found that, with 4 reference angles and 12 skipped steps, we achieved the optimal unlearned image generation effect while reducing training time compared to the baseline.\n\n- In Appendix L36, Section 1.2, we discuss optimizing dynamic skipping via interpolation. With 4 reference angles, we use a threshold strategy: if the angle difference between the sample and any reference is ≤ 20°, we set skip steps to $t_{upper} = 16$; otherwise, $t_{lower} = 12$. A detailed explanation of the choice of the 20-degree threshold can be found in Section L36 of the appendix. The goal of the dynamic approach is to further improve both the generation quality and training efficiency while achieving superior unlearn results compared to the baseline, thus reducing the overall training time.\n\n- In Appendix L184, Section 2.4, we focus on dynamic acceleration effects. Applying this framework to generation, results in **Figure 4** and **Table 4** show that leveraging 3D angle continuity and neighboring angles improved reliability, quality, and reduced generation time. Additional data further validate the performance gains from dynamic acceleration.\nWe have included additional data to further validate the improvement in performance brought by dynamic acceleration.\n\n|**metrics**|**SSIM**|**LPIPS**|**PSNR**|**MSE**|\n|---|---|---|---|---|\n|zero123|0.747|0.253|13.53|0.060|\n|3ref8steps|0.768|0.242|14.29|0.049|\n|12s|0.764|0.246|14.17|0.051|\n|16s|0.761|0.253|14.04|0.053|\n|4ref8steps|0.773|0.239|14.34|0.042|\n|12s|0.770|0.242|14.27|0.044|\n|16s|0.767|0.249|14.13|0.051|\n|8ref8steps|0.767|0.233|14.57|0.047|\n|12s|0.764|0.234|14.51|0.048|\n|16s|0.761|0.252|14.41|0.049|\n---"}, {"review_id": "fbFZv0jeL9", "reviewer": "Reviewer_c4cT", "summary": "This paper aims to extend the machine unlearning problem to the task of image-to-3D generation. The authors investigate different machine unlearning objectives, including re-targeting and partial unlearning. To handle the problems, the authors propose a skip-acceleration mechanism built on the generated multi-view images which can reduce computation but also enhance unlearning mechanisms. The experiments conducted on 3D generation models (Zero123, Zero123-XL) show that the proposed method can achieve a 30% speedup meantime unlearning the target without degrading the generation quality.", "questions": "1. **[Extension to other 3D generation models?]** \nI find that the proposed method can be applied to Zero123 and Zero123-XL. What about other 3D generation models? For example, transformer-based 3D generation models (LRM, LGM, GS-LRM, and etc); other diffusion-based 3D generation models (SV3D, and etc).\n 2. **[More qualitative results.]** The visualization results in the current version is not convincing enough. The figures in the manuscript and the supplementary materials are kind of small. Visualization results with higher resolution are required. Otherwise, it will be hard for the readers to judge whether the image quality is good or not. Furthermore, the visualization results of different viewpoints (Fig. 3) should also be provided, because the paper aims to handle the unlearning problem in 3D generation.\n 3. **[Unlearning baselines.]** The authors only provide the ablation results of the original model with or without the proposed unlearning method. The comparison with existing unlearning methods is missing. I understand that this is the first paper that extends unlearning to 3D generation. But what about the existing unlearning method on 2D generation models? Since the diffusion-based Zero123/Zero123-XL are used, could you compare the proposed method with existing unlearning method on diffusion generation models?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength：**\n 1. The authors firstly investigate the unlearning problem in 3D generation.\n 2. The proposed skip-acceleration mechanism is meaningful, which incorporates the image similarity between different generated views to avoid redundant computations.\n\n**Weakness:**\n 1. The presentation of the core method could be improved with a figure summarizing all key components.\n 2. I wonder if the proposed unlearning method in 3D generation can be applied to other 3D generation models except Zero123/Zero123-XL. Because there are many other 3D generation models, for example: transformer-based 3D generation models, LRM, LGM, GS-LRM, and etc; diffusion-based 3D generation models, SV3D, and etc.\n 3. More visualization results should be provided. The current version is not convincing enough. For example, the generated results for different unlearning tasks provided in Figure 3 only provide one single view. Since this paper aims to handle the unlearning problem in 3D generation, the authors should also provide the rendered results (i.e. rendered videos with higher resolution) on different views to prove the multi-view consistency.\n 4. The number of unlearning baselines is too few to prove the superiority. In Table 2, the authors only provide the comparison between the model with or without the proposed unlearning method. Is there any 2D unlearning methods that can be transfered to Zero123/Zero123-XL directly?", "was_revised": true, "final_justification": "After reading the response and others' reviews, most of my concerns have been solved. I will raise my rating to positive but keep it borderline.", "rebuttal": "Thank you for your constructive comments! We have carefully considered your suggestions and would like to provide the following clarifications. We'll update the preprint in later revisions.\n\n---\n\n**W1. The presentation of the core method could be improved with a figure summarizing all key components.**\n\n\nThank you for your suggestion. Indeed, we currently do not have a comprehensive diagram that presents all the key components. However, in **Algorithm 2: Unlearning via Dynamic Acceleration with Remain and Forget Losses** in the appendix, we demonstrate how all the critical components are interconnected. Based on your feedback, we will add a diagram to visually present the entire framework. This diagram will include all the requirements mentioned in **Algorithm 2**, as well as the balancing relationships among the four loss functions, and it will also incorporate **Algorithm 1: Dynamic Skipping via Interpolation** from the appendix.\n\n---\n\n**W2: I wonder if the proposed unlearning method in 3D generation can be applied to other 3D generation models except Zero123/Zero123-XL. Because there are many other 3D generation models, for example: transformer-based 3D generation models, LRM, LGM, GS-LRM, and etc; diffusion-based 3D generation models, SV3D, and etc.**\n\n\nThank you for your suggestion. Due to time constraints, we have not included sufficient experiments in this area. In fact, we have migrated to Free3D:\n\nTo validate the portability, we have conducted adaptation experiments on the Free3D model. We found that Free3D's viewpoint control mechanism essentially relies on generating structured conditional signals (such as pose encoding and ray embedding) from the input camera angles (e.g., azimuth and elevation) through deterministic transformations (e.g., trigonometric functions, spherical coordinate mapping). This characteristic is highly compatible with the angle-based input control approach used in our Unlearn framework.\n\nBased on this, we extracted the UNet backbone network from Free3D and integrated it into our training framework. During the training process:\n\n- We followed the original preprocessing pipeline of Free3D, combining multiple viewpoint angles and converting them into contextual conditions (including cross-attention inputs and ray embeddings) that match its format.\n\n- We used a multi-view alignment strategy for supervision, where the source object (e.g., Minion) → target object (e.g., Doraemon) is aligned. This forces the model to output reasonable images of the target object from any viewpoint when given the source object as input.\n\nAfter training, we re-integrated the fine-tuned UNet into the original Free3D inference pipeline. The experimental results show:\n\n- When the source object is input, all generated viewpoints consistently reflect the appearance of the target object, and viewpoint consistency is well-maintained, successfully achieving \"unlearning.\"\n\n- For other objects that were not part of the training (e.g., cars, chairs), the generated quality was comparable to the original model, with no performance degradation.\n\nAdditionally, our dynamic skipping acceleration strategy based on viewpoint similarity also demonstrates good generality. Since Free3D uses iterative samplers like DDIM, and viewpoint changes are continuous, this acceleration method can be directly applied to its inference process, significantly improving efficiency while maintaining generation quality.\n\nIn the future, we will supplement the results with visualizations using the Free3D model.\n\n---\n\n\n**W3: More visualization results should be provided. The current version is not convincing enough. For example, the generated results for different unlearning tasks provided in Figure 3 only provide one single view. Since this paper aims to handle the unlearning problem in 3D generation, the authors should also provide the rendered results (i.e. rendered videos with higher resolution) on different views to prove the multi-view consistency.**\n\n\n\nThank you for your feedback. In **Figure 2** of the appendix, titled \"Demonstration of Multi-perspective Effects on the Forget Set for Different Unlearning Tasks,\" we provide the multi-view results for each unlearning task shown in **Figure 3** of the main text. Due to space and aesthetic considerations, we did not include the multi-view images in the main body. Additionally, in **Appendix L176, Section 2.3: 3D Reconstruction Demonstrations**, we have included screenshots of the 3D depth-rendered videos. Unfortunately, the video itself was not uploaded with the supplementary materials due to oversight. However, please rest assured that we can provide the video in the future, demonstrating the rendered 3D video and its unlearning 3D consistency.\n\n---\n\n**W4: The number of unlearning baselines is too few to prove the superiority. In Table 2, the authors only provide the comparison between the model with or without the proposed unlearning method. Is there any 2D unlearning methods that can be transfered to Zero123/Zero123-XL directly?**\n\n\n\nAs you rightly pointed out, our work is the first to explore 3D unlearning, and thus our approach can be seen as a flexible framework that integrates various 2D unlearning methods with dynamic skipping acceleration to achieve effective unlearning in diffusion-based 3D models. As mentioned in our second response, we have demonstrated how this framework can be adapted to other 3D models, and we are actively exploring the transferability of more 2D unlearning methods. While the number of unlearning baselines presented in this work is currently limited, our goal is to establish a benchmark that will serve as a foundation for future research and inspire the community to further investigate novel unlearning strategies in both 2D and 3D settings. We believe this will open new avenues for innovation in the field, despite the current scope of our comparisons being limited.\n\n----\n\n**Q1 [Extension to other 3D generation models?]**\nPlease refer to W1.\n\n**Q2 [More qualitative results.]**\nPlease refer to W3.\n\n**Q3 [Unlearning baselines.]**\nPlease refer to W4.\n\n---"}, {"review_id": "ZPNg9Es92l", "reviewer": "Reviewer_b1Dt", "summary": "In this work, the authors introduce a skip-acceleration mechanism, which leverages the similarity between multi-view generated images to bypass redundant computations. By establishing coherence across viewpoints during acceleration, the framework not only reduces computation but also enhances unlearning effectiveness, outperforming the non-accelerated baseline in both accuracy and efficiency. They conduct extensive experiments on the typical 3D generation models (Zero123 and Zero123XL), demonstrating that the approach achieves a 30% speedup, while effectively unlearning target concepts without compromising generation quality. The framework provides a scalable and practical solution for privacy-preserving 3D generation, ensuring responsible AI deployment in real-world applications.", "questions": "1. How about the failure cases? What is the typical failure cases look like?\n\n2. How long does it take to generated a single image?", "limitations": "1. The evaluation is insufficient. In table 2, the authors only compared using 10 hand-crafted cases. I think a large scale benchmark will be more satisfactory.\n\n2. The authors proposed 4 lossed in total. However, how to balance these loss functions is not clearly explained or supported by experiments in the paper.", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. The idea is novel. Machine unlearning is getting more and more attention as it helps with privacy and safety. Introducing machine unlearning in novel view synthesis is important, and useful.\n\n2. The qualitative results in Figure 3 are impressive to demonstrate the effectiveness of the proposed method.\n\n3. The authors provide codes in the supplementary materials, which is extremely import for reproduction. Also, the authors provide implementation details in supplementary materials, section 2, which make this work easier to be reproduced.\n\nWeakness:\n\n1. Although this paper is almost the first in the field of machine unlearning in novel view synthesis, the authors have not compared with any baselines, which is not very solid.\n\n2. The authors does not show 3D meshes or rendered videos in supplementary results. I think using solely single-view images to demonstrate the results are not very good for demonstrate the full 3D results of this work.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your constructive comments! We have carefully considered your suggestions and would like to provide the following clarifications. We'll update the preprint in later revisions.\n\n---\n\n### **Response to the weaknesses**\n\n**W1. Although this paper is almost the first in the field of machine unlearning in novel view synthesis, the authors have not compared with any baselines, which is not very solid.**\n\n\n\nAs you rightly pointed out, our work is the first to explore 3D unlearning, and thus our approach can be seen as a flexible framework that integrates various 2D unlearning methods with dynamic skipping acceleration to achieve effective unlearning in diffusion-based 3D models. As mentioned in our second response, we have demonstrated how this framework can be adapted to other 3D models, and we are actively exploring the transferability of more 2D unlearning methods. While the number of unlearning baselines presented in this work is currently limited, our goal is to establish a benchmark that will serve as a foundation for future research and inspire the community to further investigate novel unlearning strategies in both 2D and 3D settings. We believe this will open new avenues for innovation in the field, despite the current scope of our comparisons being limited.\n\n\n\n**W2: The authors does not show 3D meshes or rendered videos in supplementary results. I think using solely single-view images to demonstrate the results are not very good for demonstrate the full 3D results of this work.**\n\n\nThank you for your feedback. In **Figure 2** of the appendix, titled \"Demonstration of Multi-perspective Effects on the Forget Set for Different Unlearning Tasks,\" we provide the multi-view results for each unlearning task shown in **Figure 3** of the main text. Due to space and aesthetic considerations, we did not include the multi-view images in the main body. Additionally, in **Appendix L176, Section 2.3: 3D Reconstruction Demonstrations**, we have included screenshots of the 3D depth-rendered videos. Unfortunately, the video itself was not uploaded with the supplementary materials due to oversight. However, please rest assured that we can provide the video in the future, demonstrating the rendered 3D video and its unlearning 3D consistency.\n\n----\n\n### **Response to the questions**\n\n**Q1: How about the failure cases? What is the typical failure cases look like?**\n\n**A1:**\nIndeed, there can be some failure cases, particularly when the quality of the override image is low. In such cases, the depth and spatial information from the viewpoint are unclear, and the semantic scalability is weak, which makes successful unlearning more challenging. For instance, when we attempted to unlearn by placing a vase on a side-view distorted chair, we found it difficult to achieve optimal results despite multiple adjustments. We will include a detailed analysis of these failure cases in the supplementary materials.\n\n**Q2: How long does it take to generated a single image?**\n\n\n**A2:**\nAs shown in table below the baseline represents the time (1.1000 seconds) taken by Zero123 to sample an image without using dynamic skipping via interpolation. The other columns show the sampling times with different skip steps, along with the speedup ratios compared to the baseline.\n\nSample Time (s) for Different Methods\n\n|Method|Full Sample |Skip 8 Sample |Skip 12 Sample|Skip 16 Sample|\n|-|-|-|-|-|\n|Baseline (No Accelerate)|1.1000|-|-|-|\n|Accelerated (Skip 8)|1.1000|0.7709|-|-|\n|Accelerated (Skip 12)|1.1000|-|0.6459|-|\n|Accelerated (Skip 16)|1.1000|-|-|0.5193|\n|Speedup (Skip 8)|-|1.4286|-|-|\n|Speedup (Skip 12)|-|-|1.7072|-|\n|Speedup (Skip 16)|-|-|-|2.1210|\n\n\n----\n\n### **Response to the limitations**\n\n**L1. The evaluation is insufficient. In table 2, the authors only compared using 10 hand-crafted cases. I think a large scale benchmark will be more satisfactory.**\n\nThank you for your valuable feedback. We acknowledge that the evaluation in Table 2 is based on a limited set of 10 hand-crafted cases. However, it is important to emphasize that our work is **the first to explore 3D machine unlearning**, which introduces novel challenges and contributions to the field. In addition to the experiments in Table 2, we have also conducted further experiments to assess the generalization ability of our method across different 3D tasks, including transforming a Minion into a black-and-white style, converting a cartoon green airplane into a solid white airplane, blurring the background of the Minion (to achieve privacy protection by limiting a certain viewpoint), unlearning the airplane into a dumbbell, and placing a blanket on a sofa, among other tasks. We will provide additional visual results in the future. These additional experiments demonstrate the robustness of our approach in diverse settings. While we agree that a large-scale benchmark would provide further insights, we focused on a smaller, controlled set initially to validate the core principles. We are planning to extend our evaluation with a larger-scale benchmark in future work to strengthen the validation of our method.\n\n**L2. The authors proposed 4 lossed in total. However, how to balance these loss functions is not clearly explained or supported by experiments in the paper.**\n\n\n- **Balancing Loss Functions**: The two parameters $(\\lambda)$ and $(\\mu)$ were omitted in Equation (14), with their settings provided in Appendix Table 1: Hyperparameters for Fake Score and Generator. The equation should be updated as:\n$\\mathcal{L}{fn} = \\lambda \\mathcal{L}{fn\\ remain} + \\mu \\mathcal{L}_{fn\\ forget}$\n\n- **Loss Function Combination**: The loss functions are balanced through the adjustable hyperparameters $(\\lambda)$ and $(\\mu)$, which control the relative importance between the generator's loss (which maintains the generation quality of the remaining categories) and the fake score network's loss (which drives the forgetting of the target category). Specifically, $(\\lambda)$ emphasizes the quality of remaining category generation, while $(\\mu)$ controls the strength of the forgetting process.\n\n- **Alternating Optimization**: To ensure balance, we adopt an alternating optimization strategy, as shown in Appendix Algorithm 2. The fake score network is updated first, followed by the generator, ensuring an effective balance between the loss functions of both.\n\nAdditionally, $(\\lambda)$ and $(\\mu)$ are insensitive to different cases; after simple tuning, they yield stable results across multiple cases."}]

Author rebuttal:

Meta-review: The paper received all positive ratings from the reviewers. They acknowledged the novelty of the method and the solid evaluation of the effectiveness. The reviewers also raised some issues regarding the presentation details and some experimental results clarification. The authors have provided a careful and convincing rebuttal for those issues, which are verified and appreciated by the reviewers. Based on the overall positive tone from all the reviewers, AC finally decided to recommend acceptance of the submission. The authors are encouraged to incorporate all the new information provided in the rebuttal into the final version of the paper.
Decision: Accept (poster)