Title: EnerVerse: Envisioning Embodied Future Space for Robotics Manipulation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Manipulation

robotics

World Models
Abstract: We introduce EnerVerse,  a generative robotics foundation model that constructs and interprets embodied spaces. EnerVerse employs a chunk-wise autoregressive video diffusion framework to predict future embodied spaces from instructions, enhanced by a sparse context memory for long-term reasoning. To model the 3D robotics world, we adopt a multi-view video representation, providing rich perspectives to address challenges like motion ambiguity and 3D grounding. Additionally, EnerVerse-D, a data engine pipeline combining generative modeling with 4D Gaussian Splatting, forms a self-reinforcing data loop to reduce the sim-to-real gap. Leveraging these innovations, EnerVerse  translates 4D world representations into physical actions via a policy head (EnerVerse-A), achieving state-of-the-art performance in both simulation and real-world tasks. For efficiency, EnerVerse-A reuses features from the first denoising step and predicts action chunks, achieving about 280 ms per 8-step action chunk on a single RTX 4090. Further video demos, dataset samples could be found in our project page.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces EmerVerse, a generative robotics foundation model designed to construct and interpret embodied spaces for robotic manipulation tasks. The model uses a chunk-wise autoregressive video diffusion framework enhanced by a sparse context memory mechanism for long-term reasoning. It also integrates a multi-view video representation to address 3D grounding challenges and a 4D Gaussian Splatting (4DGS)-based data engine (EmerVerse-D) to bridge the sim-to-real gap. The policy head (EmerVerse-A) translates 4D world representations into physical actions, achieving good performance in simulation and real-world tasks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The chunk-wise autoregressive diffusion framework with sparse memory is a well-executed approach for long-term reasoning in robotic tasks. The multi-view diffusion generator and 4DGS-based data engine are well-motivated solutions to key challenges in robotics.\n\n2. The paper provides extensive experiments across multiple benchmarks (LIBERO, CALVIN) and real-world tasks, demonstrating superior performance over existing baselines. Quantitative and qualitative results strengthens the claims.\n\n3. The paper is well-written and clearly presents its methodology and results. \n\n---\n\n**Weaknesses**\n\n1. **Technical Novelty**: While EmerVerse demonstrates impressive engineering efforts and strong empirical results, its technical novelty is limited as it primarily combines existing techniques—video diffusion, multi-view rendering, and 4D Gaussian Splatting. The work’s strength lies in its system-level integration and scaling, but the core methodology does not significantly advance beyond established paradigms. The authors are suggested to clarify the key technical novelty compared to existing works.\n\n2. **Complexity & Computational Inefficiency**: While EmerVerse demonstrates strong empirical performance, its complexity and computational inefficiency raise concerns about practicality and novelty. The pipeline combines multiple heavyweight components—autoregressive video diffusion, multi-view rendering, 4D Gaussian Splatting, and a policy head—without clear justification for why this level of complexity is necessary. Inference is slow (20 seconds per multi-view chunk), making real-world deployment challenging. Crucially, the paper lacks ablation studies to show whether simpler, faster baselines could achieve comparable results, leaving the impression of an over-engineered solution rather than a fundamentally novel advance. \n\n3. **Lack of Baseline Comparisons**: The paper notably omits comparisons with recent state-of-the-art 3D-aware video generation methods like Gen3C [1], which achieves precise camera control and world consistency through explicit 3D representations. A direct ablation against 3D-consistent generators would better contextualize its contributions.\n\n\n[1] Ren, X., Shen, T., Huang, J., Ling, H., Lu, Y., Nimier-David, M., Müller, T., Keller, A., Fidler, S. and Gao, J., 2025. Gen3c: 3d-informed world-consistent video generation with precise camera control. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 6121-6132).\n\nQUESTIONS:\n1. What is the core technical contribution beyond combining existing techniques?\n\n2. How much does each component (sparse memory, multi-view, 4DGS) contribute to performance?\n\n3. How sensitive is performance to different camera poses?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes *EnerVerse*, a generative foundation model for robotic manipulation that predicts embodied future spaces via a chunk-wise autoregressive video diffusion framework augmented with sparse memory. It introduces Free Anchor Views (FAVs) for flexible multi-view 3D scene modeling and integrates a 4D Gaussian Splatting pipeline (EnerVerse-D) to reduce the sim-to-real gap. Coupled with a policy head (EnerVerse-A), the approach achieves state-of-the-art performance on manipulation benchmarks, demonstrating robust long-horizon reasoning and real-world deployment capabilities.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength:**\n\n- The paper presents a comprehensive view from data generation to video synthesis and action planning, featuring a novel chunk-wise autoregressive diffusion model with sparse contextual memory for embodied future reasoning.\n- The integration of 4DGS optimization into multi-view video generation establishes an effective data flywheel, leading to high-quality, realistic videos.\n- The proposed FAV-based embodied future space approach, linked with policy planning, enhances spatial understanding and demonstrates clear potential for robotics applications.\n\n**Weakness:**\n\nWhile the proposed work is promising, further real-world robotic experiments are necessary. These experiments would bolster the evidence that the data generation pipeline effectively mitigates the sim-to-real gap, which is one of the primary motivations for this research.\n\nQUESTIONS:\n1. Could the authors clarify why they claim that “wrist-mounted cameras complicate policy learning”? Are there any supporting studies or empirical evidence for this assertion?\n2. Does multi-view generation induce severe hallucinations in long-sequence tasks, and if so, does this directly degrade action generation quality？\n\nLIMITATIONS:\nSee weakness and questions.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis submission focuses on learning a multi-view latent space for robotic manipulation using a world modeling objective. The paper proposes three systems: EnerVerse, a diffusion world model that takes patches from prior images and predicts future patches based on a context variable, EnerVerse-A, a diffusion transformer policy trained on top of the EnerVerse latent space, and EnerVerse-D, a system for using Gaussian Splatting to generate novel views used to train the world model. Overall, this submission describes a large system. EnerVerse is tested in its ability to predict future frames and in terms of being able to learn generalizable policies. Experiments are performed in LIBERO and CALVIN in simulation as well as on a real-world AGIBOT platform.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n**Novel sparse memory mechanism.** It is proposed to subsample patches from each frame in the video history so that a longer history can be provided to the model. This idea is helpful both because it allows for a longer history and also because it serves as a data augmentation method (by randomizing which patch is selected) leading to better generalization. This design choice is ablated in Table 4, showing that having longer memory leads to a major improvement in policy learning success rate. Further, Figure 7 shows a convincing example of the world model remembering that a stove was turned on at a prior time step.\n\n**Strong multi-view video generation results.** Figure 6 depicts an example of multi-view video generation that demonstrates object consistency across multiple views in a simulated and a real-world scene. This is supported by a quantitative evaluation in Table 1.\n\n**Strong policy learning results.** The authors demonstrate that policies trained based on the latent space of EnerVerse achieve state-of-the-art success rates on the LIBERO benchmark. LIBERO specifically tests generalization to different objects, layouts, goals and background and this is a strong indication that EnerVerse learns generalizable features. Further, CALVIN results in Table 3 are on par with strong baselines.\n\n## Weaknesses\n\n**The method description is incomplete.**\n* EnerVerse: It is stated that EnerVerse is based on DynamiCrafter, but the paper does not actually specify the size of the EnerVerse model (in the number of parameters) or any training details (training steps, optimizer setting, dataset size, etc.). The only training details provided are about the EnerVerse-A policy head. Further, I believe the paper does not specify what is the content of the context variable $c$ that is used to condition the video prediction model.\n* EnerVerse-D: Section 2.2 describes the high-level idea of generating multiple views using a video prediction model, training a 4D Gaussian splat on this sequence and generating novel views from the Gaussian splat to further train the video prediction model. However, the submission does not contain specific details as to how this system was implemented. This limits the impact of this paper, as it is unlikely that researchers could reproduce the system from its description.\n\n**The experiments description is incomplete.** On line 157, it is stated “Furthermore, we constructed a dataset containing multi-view video ground truths using the Isaac Sim simulator [31].” The submission does not contain further elaboration as to the size of the simulated dataset or the diversity of the generated scenes. Therefore, it is unlikely that researchers could independently reproduce this training setup.\n\n## Minor comments:\n* Line 86: define $J$.\n* Line 93: define $v$.\n\nQUESTIONS:\nCould you add substantially more details to your description of EnerVerse world model and EnerVerse-D?\n\nWhat is the conditioning variable $c$? Is it possible to condition the world model on actions?\n\nIs it possible to use the world model to generate synthetic training data for the policy?\n\nLIMITATIONS:\nThe authors describe inaccuracies of video prediction methods and heuristic camera pose selection as the main limitations of their work in Section 5.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 1, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents EnerVerse, a robotics foundation model that is capable of generating future frames conditioned on past observations + task context, and using these future frames for action prediction to solve the task. In particular, EnerVerse uses a chunk-wise autoregressive diffusion model with sparse memory for future prediction and incorporates multi-view video generation for better 3D spatial understanding. This video generation model can be extended to action generation (EnerVerse-A) by adding a diffusion-based action head. To facilitate better training of the multi-view frame generation + action prediction, the paper proposes a data synthesis framework for multi-view video generator based on sparse observations in simulation. Experiments are performed for video generation quality and robotic task execution, and show promising improvements over prior work.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n## Strength 1\n I liked some of the experimental aspects of the work.\n* Real-world qualitative results are useful to understand the sim2real generalizability of the work.\n* Several relevant robotic action policies are compared against in Tables 2 and 3, strengthening the claims of the paper and demonstrating the usefulness of high-quality multi-view video generation for robotic tasks.\n* Additional experiments are presented to study the value of different components of the system, which are helpful to understand their impact on the robotic tasks (sparse memory in Table 4, training strategy in Table 5, and utility of multi-view rendering in Table 2).\n\n## Strength 2\nThe related work section was good and helpful to understand the relevant literature on video generation and robotics with foundation models.\n\n## Strength 3\nI appreciate that the authors have included qualitative videos of robotic task executions in the supplementary material. \n\n# Weaknesses\n\n## Weakness 1:  Paper writing clarity is poor\nThe proposed approach was quite difficult to understand given the amount of partial / missing information in the main paper. This makes it hard for a reader to grasp the idea and the core contributions.\n* **Q1:** L93 - The velocity term used for prediction is not described (in fact, the variable $v$ is used without any explanation or definition)\n* **Q2:** Section 2.2 4D Model - Lots of things are unclear here.\n\t* What is the exact architecture of the \"Multi-View Video Generator\" in Figure 3? Some details are mentioned in text (like \"spatial attention is applied ...\" in L121-122), but these are very vague.\n\t* How exactly is the 4D model trained?\n\t* What are the inputs, outputs and losses during training?\n\t* What are the sources of training data for 4D training?\n\t* How are the multi-view camera extrinsics decided during training and inference?\n* **Q3:** Section 2.2 Real-World Data Flywheels - The mechanism and motivation behind the data generator is vague.\n\t* L127 - \"utilizes sparse observations ...\" - where are these sparse observations coming from? How are these sparse observations being utilized?\n\t* L128 - What is the base model EnerVerse here? Is it the model trained for generating a single video from past video + task context? \n\t* L129 - How is EnerVerse \"fine-tuned to be capable of receiving a complete offline observation sequence\"? \n\t* L129 - 131 - \"When inferring, .... used to construct a 4D ...\" -- What does inference mean here? What are the inputs and outputs? How are they used to construct a 4D gaussian representation?\n* **Q4:** Section 2.3\n\t* How is the model trained for action prediction? \n\t* What are the loss functions?\n\t* Does the diffusion happen along with the video diffusion or are they separate?\n\t* Does the action diffusion model use past actions as inputs?\n* **Q5:** L183 - Unclear how the DynamiCrafter baseline works in this context. \n\t* DynamiCrafter is trained to accept an image and text context to generate short videos. How exactly is it adapted as a baseline to this setting with long video generation?\n\t* Does DynamicCrafter baseline get the advantage of the same training data as EnerVerse (see L154 - 156)?\n\n## Weakness 2: Experiment section feels incomplete\nWhile I liked some aspects of the experiment design, there are a lot of things missing / unclear. \n* **Q1:** Why is DynamiCrafter the only video generation baseline? Why are other models not evaluated? AVID (https://arxiv.org/html/2410.12822v2), I2VGen-XL (https://arxiv.org/abs/2311.04145), Stable Video Diffusion (https://arxiv.org/abs/2311.15127), Motion-I2V (https://dl.acm.org/doi/abs/10.1145/3641519.3657497), LivePhoto (https://link.springer.com/chapter/10.1007/978-3-031-72649-1_27), PhysGen (https://link.springer.com/chapter/10.1007/978-3-031-73007-8_21), etc. to name a few. \n* **Q2:** Multi-View Generation is stated as a key contribution (L74), but there is only a small qualitative analysis in Figure 6 to evaluate this. Why are there no quantitative evaluations and comparisons to other baselines for novel view synthesis?\n* **Q3:** One of the core assumptions here is that better video generation should result in better robotic task performance (L23 - 25). This has been completely neglected in the experiments. Ideally, multiple video generation baselines must have been evaluated in Table 1, and their corresponding performance on Tables 2 and 3 must be reported. \n* **Q4**: Tables 2 and 3 do not report standard deviations in performance over multiple trials --- how significant are the differences in performances between methods? Are they within noise margins?\n* **Q5:** The results in Tables 2 and 3 are a bit confusing because different rows use different visual inputs. It is hard to get an apples-to-apples understanding of the relative performances between methods. Is it possible to equip other methods with the depth sensor (i.e., S-RGBD)? Is it possible to equip EnerVerse with G-RGB?\n* **Q6:** Why are baselines across Tables 2 and 3 different? \n* **Q7:** L277 - Real-world experiments are only qualitative. Can we get quantitative results and comparisons to a subset of baselines in the real-world?\n* **Q8:** L312 - What is the impact of the gaussian-splatting based approach to reduce the Sim2Real gap? This has not been studied in the experiments.\n* **Q9:** There are no baselines that leverage multi-view representations. The idea of using multiple views to enhance model representations and robotic task performance is not new (e.g., MVWM: https://arxiv.org/pdf/2302.02408, RoboHorizon: https://arxiv.org/abs/2501.06605). How do these compare to the proposed approach?\n\nQUESTIONS:\nMy initial assessment is that this paper is incomplete and not yet ready to be accepted. My primary concerns are to do with the paper clarity and experimental section. I have listed the questions along with the weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-04"}