Title: Hallo3D: Multi-Modal Hallucination Detection and Mitigation for Consistent 3D Content Generation
Venue: NeurIPS 2024
Accepted: yes
Keywords: 3D Generation

Hallucination Detection and Mitigation

Multi-modal Inference

View Alignment
Abstract: Recent advancements in 3D content generation have been significant, primarily due to the visual priors provided by pretrained diffusion models. However, large 2D visual models exhibit spatial perception hallucinations, leading to multi-view inconsistency in 3D content generated through Score Distillation Sampling (SDS). This phenomenon, characterized by overfitting to specific views, is referred to as the "Janus Problem". In this work, we investigate the hallucination issues of pretrained models and find that large multimodal models without geometric constraints possess the capability to infer geometric structures, which can be utilized to mitigate multi-view inconsistency. Building on this, we propose a novel tuning-free method. We represent the multimodal inconsistency query information to detect specific hallucinations in 3D content, using this as an enhanced prompt to re-consist the 2D renderings of 3D and jointly optimize the structure and appearance across different views. Our approach does not require 3D training data and can be implemented plug-and-play within existing frameworks. Extensive experiments demonstrate that our method significantly improves the consistency of 3D content generation and specifically mitigates hallucinations caused by pretrained large models, achieving state-of-the-art performance compared to other optimization methods.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors point out that generating 3D content with Score Distillation Sampling leads to multi-view inconsistencies. To address this challenge, they propose a novel, tuning-free method called Hallo3D. Specifically, they utilize large multi-modal models (e.g., GPT-4V) to detect and correct these inconsistencies during optimization. Notably, their method can be implemented in a plug-and-play manner with several text-to-3D generation baselines, achieving satisfying visual results.\n\nSTRENGTHS:\n1.\tThe idea of using large multi-modal models to implement a generation-detection-correction paradigm for text-to-3D generation is both novel and interesting. Moreover, the experimental results demonstrate that this approach works quite well.\n\n2.\tThe entire \"Methodology\" section is presented quite well. The preliminaries are written accurately, and the subsequent subsections clearly introduce the overall motivation and design.\n\n3.\tThe experiments are also solidly conducted, including both qualitative and quantitative comparisons on text- and image-driven 3D generation. The ablation studies are sensible and include user studies as well.\n\nWEAKNESSES:\nThe overall quality of the paper is quite good, but some problems still exist:\n\n1.\tIn Figure 2, the \"Illustration of the Janus problem\" seems to understate the performance of current baselines. In the first two rows of the figure, eight faces of the dog are visible around the sculpture, which does not align with the experimental results from methods like DreamFusion. This is acceptable as an illustration, but it would be better to be more rigorous.\n\n2.\tEquation 6 in line 183 does not seem to be derivable from Equation 12 in the original DDIM paper. Please check this carefully.\n\n3.\tThe ablation study section lacks clarity. Rows 2-4 do not show a clear visual difference, and the text in Section 4.4 does not provide an easy-to-follow illustration.\n\nQUESTIONS:\n1.\tSection 3.4 is not clear enough. From lines 180-183, the authors seem to use the “DDIM Inversion” technique to regenerate an image without the so-called hallucination. However, generating such an image with CFG without damaging the original structure of the image is not an easy task (see Null-text Inversion). More illustration of this part should be provided.\n\n2.\tFrom Figures 3 and 4, it appears that you only feed a single image into the LMM for hallucination detection. However, a less serious multi-view inconsistency (e.g., a rabbit with three eyes) can only be observed from certain view directions. How can the effectiveness of Hallo3D be proven in this context?\n\n3.\tThe generation-detection-correction paradigm introduced in this paper appears to require frequent use of the LMM. Given that generating 3D content with SDS optimization typically requires around 5,000-10,000 iterations, what is the overall time cost of your algorithm?\n\nI’m willing to give this paper a higher rating if these three questions are well addressed!\n\nLIMITATIONS:\nYes, the authors discuss the limitations of the proposed Hallo3D clearly at the end of the \"Conclusion and Discussion\" section.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nHallo3D presents a tuning-free method, empowering 3D content generation frameworks via multi-modal LLM. The paper aims to solve the hallucination and inconsistent problems in SDS-based 3D content generation pipelines. With the design of multi-view appearance alignment and enhanced negative prompts, Hallo3D is able to improve the quality of generated 3D objects. The effectiveness of the proposed method was demonstrated through experimental verification using multiple frameworks.\n\nSTRENGTHS:\n1. The paper integrates various SDS-based 3D generation frameworks, including text-to-3D and image-to-3D frameworks. And the effectiveness of the proposed method on these frameworks has been demonstrated through qualitative results.\n2. Introducing multi-modal LLM to optimize the generation of diffusion is effective, and the output of multi-modal LLM can be directly applied to the diffusion through negative prompts. This design is concise and clever.\n3. The paper also performs some quantitative results to validate the effectiveness of the proposed method.\n4. The design is flexible and adaptable to the rapid advancements in SDS-based methods.\n\nWEAKNESSES:\n1. The method description is not clear enough.\n- In lines 148-150, the paper claims defining a new denoising strategy, aiming to enhance the appearance consistency. But there's no further details about how to apply the new denoising strategy with appearance attention.\n- Lines 184-186 provide some description, but lack a detailed explanation of how to utilize appearance attention, which is an important contribution claimed by the paper.\n2. The ablation study lacks quantitative analysis.\n- Figure 7 presents qualitative results; however, the lack of quantitative analysis makes these results less convincing.\n3. Lack of enough quantitative analysis showcasing performance improvements on image-to-3d frameworks.\n- Image-to-3D models can use images rendered from original 3D objects as input and ground truth, and then use the metrics of 2D images for measurement, such as SSIM, PSNR, etc. The GSO dataset or Objaverse dataset can be used.\n4. The impact of methods on training time is not introduced. \n- The paper refine 3D contents generated by baseline models, but the impact of training time is not addressed.\n\nQUESTIONS:\n1. What does consistency refer to in the paper? The SDS-based method is optimized to generate a complete 3D content, not like directly using 2D diffusion models to generate multi-views. Does inconsistency refer to Janus problem?\n2. Why need an additional $L_{cg}$ loss? Why not add negative prompts directly to the 2D diffusion models in the top left of Figure 3 for $L_{SDS}$ loss?\n3. Is it necessary to first use the baseline methods to obtain a raw 3D content before cooperating with Multi-modal LLM for subsequent operations.\n\nLIMITATIONS:\nThe paper has addressed the potential negative societal impact: the potential misuse of advanced 3D generation technologies could undermine social trust and compromise information integrity. And there are no other obvious potential social impacts.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper aims to alleviate the multi-Janus problem in SDS-based 3D generation tasks. Inspired by the spatial structure inference capability of large multimodality models (LMMs), they propose a novel automatic negative prompt strategy. Specially, they input rendered images and 3D-aware inquiry prompts to LMM to obtain negative prompts. To keep the semantic consistency, they regenerate rendered image guided by a negative prompt and calculate regularization loss between the originally rendered image and the regenerated one.\n\nSTRENGTHS:\n- A novel and interesting strategy to introduce LMM automatically generating negative prompts to alleviate Janus issue.\n    - Different from direct use generated negative prompts, this work introduces a prompt-enhanced re-consistency scheme that regenerates render image guided by negative prompt and calculates an MSE loss to help SDS optimization.\n- Multi-view Appearance Alignment module calculates self-attention between the focal view of noise x and others to introduce appearance consistency across different views.\n- The proposed method is applied to various baselines, including different 3D representations (NeRF and Gaussians) and tasks (text-to-3D and image-to-3D), to show robustness and generalization ability.\n\nWEAKNESSES:\n- Lack of detaild of implementation and ablation study. I doubt the reproduction of this work.\n    - For Multi-modal Hallucination Detection, is P_I  always the same as the prompts in Figure 4? All rendered images are inputted to LMM?   Which LMM is used in the final experiments, given GPT-4V and LLaVA in Figure 4? \n    - For Prompt-Enhanced Re-consistency, how to balance loss_SDS and loss_CG? Can you provide the loss curve of them? The author mentions that this module only works when the rendered images exhibit complete semantic structures. So what is the proportion in the training does it work on average? Why Dψ can produce \"None\"? I cannot find the corresponding instruction in Figure4's prompts.\n    - For the Ablation Study, does w/o B mean that there are no adaptive negative prompts and a general negative prompt is used in the Prompt-Enhanced Re-consistency module to calculate L_CG? So what is the general negative prompt in w/o B ablation study? In w/o C study, does this mean that the generated negative prompt is directly used in equation (3) and no L_CG?\n\n- Lack of some necessary comparison.\n    - Please show the training time compared with multiple baselines. In my aspect, inference LMM for each iteration may introduce more training time.\n    - The ablation study and analysis are not complete. It's interesting to show that replacing P_E^− with a general negative prompt. And why not introduce L_CG all the time? What is the impact of P_I for Hallucination Detection?\n    - Magic3D is very similar with DreamFusion-IF except for the additional finetune phrase. Actually, I think finetune stage has less impact for geometric structures. I suggest authors replace Magic3D with ProlificDreamer. Prolificdreamer has a serious Janus problem but it produces clearer texture which is beneficial for Multi-modal Hallucination Detection.\n    - Lack of comparison with mentioned prompt engineering methods: \"prep-neg\" and \"debiasing scores and prompts\".\n\nQUESTIONS:\n- How to select a focal view in the Multi-view Appearance Alignment module is unclear. Is the focal view chosen randomly? Or the front view as a focal view always be chosen for each iteration? \n- Why the shown examples are low quality? Can the proposed method work for high-quality 3D generation?\n- Why only provide one 360-degree example? Why not consider providing complete results in an appendix?\n\nLIMITATIONS:\nBased on the provided examples, it seems like this work just works for very low-quality 3D generation and simple prompts.\n\n---------------------\n\nThis work presents the integration of VLM into text-to-3D generation, framing the Janus problem as hallucination detection. It automatically produces adaptive negative prompts via VLM. While the effect is constrained, this approach, devoid of training and 3D priors, offers insights into leveraging LLM in 3D generation. The primary constraint lies in the additional time overhead.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-17"}