Title: Context Preserving Autoregressive Frame Generation for Bounded Video
Venue: NeurIPS 2025
Accepted: no
Keywords: Generative model; diffusion model; video generation
Abstract: Recently, various video generation methods have been proposed, as diffusion models demonstrate their superior ability to generate high-quality videos. Specifically, autoregressive approaches have been suggested to enable the generation of videos of arbitrary length. However, the methods are not suitable for bounded video generation, as they generate open-ended videos. 
Moreover, recent methods for bounded video generation rely on flipping frames to satisfy the boundary constraint imposed by the ending frame. However, this approach contradicts the inherent bias of video models to generate frames in forward direction, limiting the generation capability. Accordingly, we propose a novel autoregressive approach for bounded video generation. Specifically, we introduce a context-aware bidirectional denoising method that progressively generates frames in both forward and backward directions while considering the frame context. Then, we propose a method to mitigate the context gap between the two directions, to ensure smooth and coherent transition between the sequences. Experimental results demonstrate the superiority of our approach over previous methods. Specifically, as our method aligns with the video model's forward generation bias, the output videos present more realistic motion dynamics. Moreover, our method outputs frames with enhanced visual quality by maintaining a consistent frame length for model input. More results can be found in our project page


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a training-free method to interpolate between the starting and ending frames. Specifically, it uses uneven noise level to progressively predict the forward latent bidirectionally, and proposes a fusing mechanism for the two predicted frame sequences. It further adopts the SVGD guidance to improve generation quality. Qualitative experiments show that it can solve the problem more effectively, especially for the case with irreversible motion.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The proposed method is straight-forward and easy to follow.\n2. The qualitative experiment and the webpage show clear advantage of proposed method against baselines.\n3. The proposed method can generate interpolated video of arbitrary length, rather than fixed length.\n\nWeaknesses:\n1. The technical contribution is marginal. The method is essentially a mixing of forward/backward generating sequence, with SVGD guidance. The method of adding more noise in the direction of generation has been studied in prior works [1].\n2. Although the authors claim to avoid flipping the video frames to reduce generation bias, adding more noise in the direction of generation is also out of distribution for the base generative models, and this bias might affect generation quality for a training-free method. The authors should elaborate more on this.\n3. The evaluation experiment seems problematic. For example, FIFO achieves the best score on Visual Quality, but why is the score of \"Ours\" bolded? Same case ViBiD on MAE(loop). The authors should provide a brief analysis on the under-performing metrics. On the other hand, human-evaluated metrics will be appreciated, which may be more reliable.\n4. The adopted SVGD guidance seems independent of the main algorithm. The authors should equip the baselines with the same guidance, or remove the guidance to better analyze the performance gain against existing methods.\n5. Compared to training-free methods, training-based methods may be more effective. And this paper lacks comparison with prior works such as [2][3][4][5]\n\nReferences:\n\n[1] Rolling Diffusion Models\n\n[2] LDMVFI: Video Frame Interpolation with Latent Diffusion Models\n\n[3] FILM: Frame Interpolation for Large Motion\n\n[4] Animating Open-domain Images with Video Diffusion Priors\n\n[5] Adapting Image-to-Video Models for Keyframe Interpolation\n\nQUESTIONS:\nPlease refer to the Weakness part. Other questions/minor issues:\n1. Can the authors clarify on the differences between bounded video generation and video interpolation (which may be more conventional) ?\n2. Some notations are confusing. For example, $\\tilde{z}_j$ in Algorithm 1 has not been explained.\n3. Can the authors provide some failing cases? From Table 1 we can see that the proposed method underperforms some baselines on certain metrics. However, the qualitative comparisons only demonstrate the good case.\n\nLIMITATIONS:\nThe base video generation model largely limits the generation quality as well as the length of the output video. To better assess the ability of the proposed method, I suggest the authors equipped it with more advanced (with more parameters) backbones.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a training free method that enables video generators to adhere to boundary conditions including, among others, last frame or first and last frame conditioning. To overcome limitations of previous methods that model reverse temporal dynamics, the authors propose a procedure, similar to \"Diffusion Forcing\" that progressively denoises video frames simultaneously from both video sides. Context-aware bidirectional autoregressive denoising and a mixing procedure are proposed to respect boundary conditions while limiting abrupt transitions in the middle of the videos.\n\nSTRENGTHS AND WEAKNESSES:\nQUALITY\n- While seemingly improving over similar frameworks as shown in Tab. 1, absolute quality of the results is low, with the produced videos presenting limited motion quality. Given the showcased level of obtainable quality, it is questionable whether the method could be used as an alternative to model fine-tuning.\n- Incorrect bolding in Tab 1 \n\n\nCLARITY\n- The framework is rather complex, requiring the usage of several tricks and hyper parameters. It is likely important for all such hyper parameters so be set up properly, but the paper does not provide enough insights or ablations showing the effects of each.\n\nSIGNIFICANCE\n- The paper addresses the problem of making a video generator adhere to given conditioning frames in a training free manner. This is a very specific value proposition from the work, whose value lies in the amount of training that can be saved to fine-tune large scale foundational video generation models to support such task natively. Performing such fine-tuning is, in practice, inexpensive, requiring only a few thousands fine-tuning iterations on limited amounts of computational resources. Such fine-tuning is thus inexpensive (especially compared to the initial cost of training a foundational video generator model) while producing high quality generators capable of respecting the boundary conditions. The proposed method is complex, requiring many hyper parameters, produces videos of limited quality and is not capable of handling the most interesting cases of boundary conditions with very different contexts.\n\nORIGINALITY\n- Originality of the work appears fair. While drawing inspiration from frameworks such as Diffusion Forcing and mixing techniques common in training free long video generation to smooth model predictions, it combines them in an original manner.\n\nQUESTIONS:\n- To improve significance, the work could show how the current framework would compare to fine-tuning of a state-of-the-art open source video generator such as Wan or CogvideoX to respect boundary conditions. The work could show the quality gap between the two solutions, and training cost for the fine-tuning solution. If such analysis reveals a small gap in the quality of the attainable results between the two approaches, the saving of computational resources for training may increase the significance of the work. Unless such analysis is shown, significance of the work remains not sufficiently demonstrated.\n\nLIMITATIONS:\nThe paper highlights limitations of the method.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel bidirectional autoregressive method for generating videos that satisfy specific boundary constraints. By considering frame context and generating frames in both forward and reverse directions, the method addresses the unnatural motion often produced by traditional approaches when handling irreversible motions. It introduces a context-aware bidirectional denoising technique, as well as frame initialization strategies and context gradient mixing methods, to bridge the context gap between the two directions and generate coherent video sequences. Experimental results demonstrate that the proposed method produces videos with more realistic motion dynamics and higher visual quality than existing approaches, particularly when adhering to boundary constraints.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1) This paper proposes a novel bidirectional autoregressive method for bounded video generation. By using context-aware bidirectional denoising, along with frame initialization and context mixing strategies to reduce context gaps, it effectively addresses coherence issues between forward and backward frame generation. These designs help mitigate the forward-generation bias commonly seen in video models, thereby enhancing the realism and dynamics of the generated videos.\n2) The proposed method outperforms existing approaches like FIFO, TRF, and ViBiDSampler in terms of visual quality, temporal consistency, and boundary satisfaction. For example, under both identical and distinct boundary conditions, the generated videos demonstrate richer visual details and more natural motion patterns, including fluid dynamics like water splashes.\n3) The paper conducts ablation studies on irreversible motion generation, video length impacts on context, and context gaps. These studies validate the effectiveness of each component of the proposed method, offering deeper insights for future research.\n\n**Weaknesses**\n1) Although the paper presents a bidirectional autoregressive method tailored for bounded video generation, the core ideas are built upon existing autoregressive and diffusion-based generative models [1] [2]. For instance, techniques such as Stein Variational Gradient Descent (SVGD) have already been explored in generative modeling [3], including for video data. While the authors introduce a novel integration and application of these ideas, the conceptual innovation appears incremental rather than fundamentally novel.\n\n2) The experimental setup may limit a comprehensive evaluation of the method’s performance. The datasets used primarily consist of short video clips with specific irreversible motions, which may not reflect performance on longer or more complex motion sequences. Furthermore, the chosen evaluation metrics may not sufficiently capture aspects such as semantic consistency or the richness of visual details, potentially underrepresenting the quality and diversity of the generated content.\n3) The paper lacks comprehensive comparisons with recent related work, which hinders a clear assessment of the method's distinct contributions and novelty in the current research landscape. Additionally, the robustness of the approach under varying motion complexities, boundary conditions, and noise disturbances is not thoroughly evaluated. This insufficient validation makes it challenging to assess the method’s reliability in diverse real-world scenarios.\n4) In Table 1 of the paper, the metric labeled 'Visual quality' is one where lower values indicate better performance. However, the proposed method is not the best according to this metric, yet it is still highlighted in bold as if it were the best.\n\n\n\n[1] Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. \n\n[2] Bin Lin, Yunyang Ge, Xinhua Cheng, Zongjian Li, Bin Zhu, Shaodong Wang, Xianyi He, Yang Ye, Shenghai Yuan, Liuhan Chen, et al. Open-sora plan: Open-source large video generation model. \n\n[3] Kim, Subin, et al. \"Collaborative score distillation for consistent visual synthesis.\"\n\nQUESTIONS:\n1) Although the authors have demonstrated the advantages of the proposed method in two tasks, the evaluation of video generation under different scenarios remains insufficiently comprehensive. Beyond the reported cases, did the authors assess other video lengths? Is the method's performance stable across a broader range of video durations? Is it accurate in more complex scenes? It is recommended that the authors include additional experimental results on videos of varying lengths to more convincingly demonstrate the robustness and generalization capability of the method. \n2) While the ablation studies support the effectiveness of the proposed method in bridging context gaps, the analysis of the root causes of these gaps and the method’s sensitivity to such gaps under different scenarios remains limited. The authors are encouraged to further investigate the mechanisms behind the formation of context gaps and to evaluate the method’s performance under varying degrees of context mismatch, in order to better understand its strengths and limitations.\n8. Limitations\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper targets bounded video generation—producing a finite video that must respect given start and/or end frames—and observes that existing diffusion‑based methods typically create the reverse segment by simply time‑flipping forward‑generated frames, an approach that clashes with the innate forward‑sampling bias of current video diffusion models and yields unrealistic motion for irreversible actions. To solve this, the authors propose a training‑free, bidirectional autoregressive framework that denoises frames progressively in both forward and backward directions using a “reversed‑diagonal” timestep schedule, thereby generating the reverse sequence without flipping. They introduce a context‑aware gradient (inspired by Stein Variational Gradient Descent) that lets each frame share denoising updates with its neighbors and a global mixing strategy that blends latent predictions from the two directions to eliminate the mid‑sequence discontinuity (“context gap”). A three‑phase inference schedule alternates look‑ahead denoising while keeping the model’s input length constant, avoiding the length mismatch that degrades prior single‑pass methods. Experiments on VideoCrafter‑2 across 400 GPT‑generated prompts show that, compared with FIFO (open‑ended) and flip‑based bounded video baselines (TRF, ViBiDSampler), the proposed method achieves comparable or better temporal consistency (VBench flicker/background scores), better perceptual quality (lower FVD/FID), more natural irreversible motions, and similar boundary adherence, all without extra training.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n-- Bounded generation is important for interpolation / completion but under‑studied, and the flip‑based limitation is well explained.\n\n-- The proposed reversed‑diagonal trick to sample backward without re‑training is simple and effective.\n\n-- Context‑gradient idea provides a principled way to let neighboring frames communicate; ablations indicate gains.\n\n-- Qualitative evidence on irreversible motions convincingly shows the benefit over flip methods.\n\nWeakness:\n\nThe biggest concern I have for this paper is its timelyness -- it still uses VideoCrafter 2 as its backbone, and VideoCrafter 2 itself is known for having low FPS and strong artifacts -- it's 1.5 years old already. Concurrent work that share similar ideas, such as FramePack, already started doing experiments on Wan/Hunyuan. The authors show some comparisons on their website, but firstly there are not that many, secondly it is difficult to argue about the quality gain, the examples all exhibit strong artifacts...\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}