Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
Authors: Shanchuan Lin
Ceyuan Yang
Hao He
Jianwen Jiang
Yuxi Ren
Xin Xia
Yang Zhao
Xuefeng Xiao
Lu Jiang
Venue: NeurIPS 2025 poster ()
Keywords: video generation
real-time
interactive
diffusion model
adversarial
gan
Primary area: deep_learning
TL;DR: Real-time interactive streaming video generation. 24fps up to a minute long.

Abstract: Existing large-scale video generation models are computationally intensive, preventing adoption in real-time and interactive applications. In this work, we propose autoregressive adversarial post-training (AAPT) to turn a pre-trained latent video diffusion model into
a real-time, interactive, streaming video generator. Our model autoregressively generates a latent frame at a time using a single neural function evaluation (1NFE). The model can stream the result to the user in real time and receive interactive responses as control to generate the next latent frame. Unlike existing approaches, our method explores adversarial training as an effective paradigm for autoregressive generation. This allows us to design a more efficient architecture for one-step generation and to train the model in a student-forcing way to mitigate error accumulation. The adversarial approach also enables us to train the model for long-duration generation fully utilizing the KV cache. As a result, our 8B model achieves real-time, 24fps, nonstop, streaming video generation at 736x416 resolution on a single H100, or 1280x720 on 8xH100 up to a minute long (1440 frames).

Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation
Shanchuan Lin"
Ceyuan Yang
Hao He
Jianwen Jiang'
Yuxi Ren
Xin Xia
Yang Zhao
Xuefeng Xiao
Lu Jiang
ByteDance Seed
https://saweeddapp.comm/
Abstract
Existing large-scale video generation models are computationally intensive, pre- venting adoption in real-time and interactive applications. In this work, we propose autoregressive adversarial post-training (AAPT) transform a pre-trained latent video diffusion model into a real-time, interactive video generator. Our model autoregressively generates a latent frame a time using single neural function evaluation (INFE). The model can stream the result to the user in real time and receive interactive responses as controls generate the next latent frame. Un- like existing approaches, our method explores adversarial training an effective paradigm for autoregressive generation. This not only allows us design an architecture that is more efficient for one-step generation while fully utilizing the KV cache, but also enables training the model student-forcing manner that proves to be effective in reducing error accumulation during long video generation. Our experiments demonstrate that our 8B model achieves real-time, 24fps, stream- ing video generation at 736 x416 resolution single H100, x280x720 on 8xH100 up to a minute long (1440 frames).
In recent years, the field of visual content creation has been transformed by the rise of foundation models for video generation 4 78 69 95]. These models have enabled wide range of powerful applications, including text-to-videe generation, maage-to-video synthesis, and controllable video creation conditioned on various multi-modal signals.
Building this progress, researchers beginning to explore more ambitious applications. One exciting direction is using video generation models as interactive game engines and world simula- tors 67 4] Unlike offline video synthesis, interactive video generation requires the model to respond to user inputs in real time and continuously generate coherent video as the world evolves.
While diffusion models produce high-quality videos, they very expensive for real-tine real-time interactive video generation. Early approaches applied diffusion models frame-by by-frame [93] [[T]. However, these approaches incur high redundancy due the need to reprocess the context frames at every frame generation step. To address this, diffusion forcing 7 108 40 23 introduced progressive noise parallelize denoising across frames. Recent work further reduced inference costs by incorporating causal attention, KV caching, and step distillation 117 75), with the current best model 117 achieving four denoising steps.
cneeration-popllaiie by large language models (LLMs) 5 I offers an alternative. Models like VideoPoet [4] treat video generation as a next-token prediction task, which can straightforwardly leverage KV caching to improve generation
Meanwhile, bosen- autoregressive
"Shanchuan Lin: Corresponding author: peter in@bytedance. com Hao He: The Chinese University of Hong Kong. Internship at ByteDance Seed. Jianwen Jiang: ByteDance Intelligent Creation Lab.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

efficiency. However, per-token decoding remains sequential, limiting parallelism and making it difficult to meet real-time demands.
In this work, we aim to address the three core challenges of interactive video generation: (1) achieving real-time video generation throughput, (2) maintaining a low latency for interactive signals, and (3) enabling causal video generation of an extended duration. To this end, we explore adversarial training as new paradigm and propose autoregressive adversarial post-training (AAPT) as an effective strategy for transforming a pretrained video diffusion transformer into a highly efficient autoregressive generator.
Our approach offers several advantages. First, it is fast. Our model autoregressively predicts each latent frame in single forward pass (INFE) while fully exploiting the KV cache. Our architecture design further enables 2x higher efficiency than equivalent diffusion-forcing models distilled to one step. Second, it maintains better quality over long durations. Our adversarial approach enables full student-forcing training, which mitigates error accumulation for long video generation. Furthermore, our suueett-frrcigg approach does not require paired ground-trute targets, allowing us to train long video generators and bypass the limitations of short-duration training data. This is important, as single continuous shots of tens of seconds are extremely rare in most datasets.
We demonstrate these benefits empirically. In terms of speed, our 8B- 2B-pammmeter model achieves real-time 24fps video generation at 736 736 resolution
single H100 GPU, and 1280 720 resolution on 8 x H100 GPUs, with a latency of only 0.16 seconds, substantially outperforming CausVid [17], a 5B model that operates 640x352 9.4fps with a 1.30-second latency. In terms of duration, our model can generate continuous 60-second (1 (1400-frame) video streams while fully utilizing the KV cache. This significantly exceeds the previous best one-step generator, APT 49 which supports only 49 frames.
Our experiments focus on the image-to-video (I2V) generation scenario. where the first frame is provided by the user, as most interactive applications adopt this setting. We showcase our method on two interactive applications- pose- cose-conditioned virtual human generation and camera-controlle world exploration where users can steer video generation in real time through interactive inputs. Evaluations show that our model achieves performance comparable to the state of the art.
Related Work
One-Step Video Generation Early video generation models 3 using generative adversarial networks (GANs) [18] can achieve fast generation using a single network evaluation. However, the
quality, duration, and resolution are poor by modern standards. Diffusion models [28 84 are the
current state-of-thee-nte yet their iterative generation process is slow and expensive. Generating few seconds of high-resolution videos can take minutes. Existing research has attempted to reduce the inference cost by proposing more efficient formulations [51
55 35), samplers (59 60 90],
architecture 1109 121
120
119
98 17 66), caching 663
53
125
and distillation, etc. In particular,
step distillation 74
115
76
55
1TQ
g 61 56
emerges as one of the most effective approaches and has been widely studied in the image domain and is also adopted in video models. Seaweed (78) and FastHunyuan [15) report that the generation of 5-second 1280 720 24fps videos can be distilled to 8 or 6 steps without much degradation in quality. For further reduction in steps, SE-V [23] and OSV 64 explore 2 seconds of x024x768 7fps image-to-video generation using only a single step. Recently, APT 49 achieves real-time text-to-video generation of 2-second x280x720 24fps videos on 8 H100 GPUs using a single step. This has inspired more downstream applications to explore one-step video generation [99] [1]. Our method extends adversarial post-training (APT) to the autoregressive video generation scenario.
Streaming Long- Video Generation Early research in streaming and long video generation [26] 41 (6 applies training-free or pipeline approaches on small-scale image and video generation models but is limited in quality. Modern large-scale video diffusion models, e.g. MovieGen (69), Hunyuan 44 1 (95), and Seaweed (78), adopt transformer architecture and are trained on much higher resolutions and frame rates. However, due to the quadratic increase in attention computation, these models are commonly trained to only generate videos up to seconds. To support long-video generation, these models are also trained on the video extension task, which gives the model the first few frames as a condition. At inference, this allows the model to extend the generation and stream the result to users as 5-second chunks. The extension can only be performed few times before the

---

error accumulation catches up. Recent works have also explored architectures with linear complexity to directly generate long videos 998
66], but they are not designed for streaming applications.
More recently, diffusion forcing 7 has been proposed for video generation. It assigns progressive noise levels to frame chunks so the decoding proceeds in causal streaming fashion. Earlier work uses bidirectional attention [108] 40). Recent works have moved toward causal attention with KV
Most notably, SkyReel-2 [9] and MAGI-I (75 are diffusion-forcing video generation models trained from scratch. Caus Vid M explores converting existing bidirectional video diffusion models to causal diffusion-forcing generators. Some of these methods also apply step distillation to improve speed. MAGI-1 775 distills the model to 8 steps and outputs 24 frames as chunk. It reports real-time 1280x720 24fps generation on 24 x H100 GPUs However, this amount of computation limits wide adoption. Caus Vid [117] distills the model to 4 steps and outputs 16 frames as a chunk. It can generate 640x320 videos at 9. 9.4fps on a single H100 GPU. In comparison, our method is significantly faster. Our model uses only a single step and achieves 24fps streaming at 736x 416 resolution on a single H100 GPU, or x280x on HX00 GPUs. Moreover, ours generates a single latent frame (4 video frames) at time to minimize latency.
cache [117 9 75 23
is important note that these diffusion-foreing models are still only trained up to a fixed-duration window, e.g. 5 seconds. Early approaches without KV cache can run a sliding window, but this becomes an issue for KV cache because the receptive field grows indefinitely. Applying a sliding window and dropping out KV tokens can't 't help because the remaining tokens in the cache were computed in the past and still carry the receptive field. Naive extrapolation at inference leads to behaviors. Therefore, methods like Caus [177] SkyReel-V2 V2 (9), and MAGI- 1 75) still need to apply the extension technique at inference by restarting and re-computing some overlapping context frames to generate long videos. Except that the diffusion forcing objective naturally supports input tokens with different noise levels, so the context frames can be given as clean latent frames at the beginning, with additional training necessary. However, this is not ideal as it causes wait time on real-world streaming applications. In contrast, our method supports streaming generations of minute-long videos using KV cache without stopping and reprocessing.
LLMs for Video Generation Large language models (LLMs) 5 19 have widely adopted the
causal transformer architecture 94 for autoregressive generation. Most notably, attention is masked to prevent attending to future tokens, the inputs are past predictions, and the output targets are shifted by one for predicting the next tokens. Recent research has shown that images and videos can also be generated in such an autoregressive fashion [87] [02 106, [0]. Although causal generation with KV
cache is computationally efficient, generating token- ooken-by-toker by-token prevents parallelization and is slow for high-resolution generation. Some research has explored the decoding of multiple tokens at once during inference [003] IT, but there is a tradeoff for quality, and it is challenging to decode an entire frame at once. Our architecture is inspired by LLMs, but ours generates frame of tokens at time, trained using an adversarial objective. This is optimized for fast generation.
Interactive Video Generation Our paper showcases our model's real-time interactive generation ability on two applications: pose-controlled virtual human video generation and camera-controlled world exploration. We briefly introduce the related works in each subfield.
Recent research has explored the use of video generation models to create interactive environments for gameplay and world simulation 2 6 93 22 16 13. Typically, the first frame is given, and the model continuously predicts the next frame given user control (image-to-world), The control can be the discrete states in an action space or general-purposs camera position embeddings [24 25). However, the high computation cost of the existing video generation approaches greatly limits the resolution and frame rates. For example, GameNGen [3] and MineWorld [22 only generate videos around 320x 240 resolution at 6~20fps with small models of a few hundred million parameters. Recent works, e.g. Genie-2 [67,, Oasis [3], Matrix [6). etc., have moved toward large-scale architectures and higher resolutions. Though many report their methods can operate in real-time, the specific hardware requirements are not specified.
Interactive video generation also holds significant potential in the domain of virtual humans. Typically. the first frame is given to establish the identity, then the pose 30 62 or other multimodal [34 46 45 89 conditions are given to drive the subject. Existing works employ diffusion models with the extension technique to generate long videos (85). The inference speed remains a major bottleneck that limits their applicability to offline human video generation tasks.
3

---

Ouiput
Logit
Logit
Generatio Transfommer
Discriminsior Trans
(Blicc Causal Attntion)
(Iloock Causal Altarion
Channel Concat
Test
Noise
NNoss
Noose 3
Test
Frame
Frnme
Frame
0000
Condition
Connition 2
Condition 3
Condition 0
Condition
Condition 2
Frame
Trame
Trmme
Framc B (User prosides)
Frame
Trame
Imput
(User provide))))
Figure 1:
Generator (left) is a block causal transformer. The initial frame 0 is provided by the user at the first autoregressive step, along with text, condition, and noise as inputs to the model to generate the next frame in single forward pass. Then, the generated frame recycled as input, along with new conditions and noise, to recursively generate further frames. KV cache is used to avoid recomputation of past tokens. A sliding window is used ensure constant speed and memory for the generation of arbitrary lengths. Discriminator (right) uses the same block causal architecture. Condition inputs are shifted align with the frame inputs. Since is initialized from the diffusion weights, we replace the noise channels with frame inputs following APT.
2 Method
Step
AR Skep
AI Step
Our objective is to transform a pre-trained video diffusion model into a fast, per-latent-frame causal generator suitable for real-time interactive applications. We achieve this through a new method called autoregressive adversarial post-training (AAPT). This section discusses AAPT's architectural transfor- mations and training procedures.
DF(sstep
Compute
Compute
Compute
Ounx
2.1 Causal Architecture
We build our method on a pre-trained video diffusion model that employs diffusion transformer (DiT) [68] architecture and operates in a spatially and temporally compressed latent space through 3D variational autoencoder (VAE) [18]. Since our model operates in the latent space, we will refer to latent frames simply frames unless otherwise specified. Our diffusion transformer has 8 billion (8B) parameters. It takes text embedding tokens, noisy visual tokens, and diffusion timesteps as input, and calculates bidirectional full attention over all the text and video tokens.
Computo
Compune
Compune
Figure 2: Ours is more efficient
than one-step diffusion forcing (DF).
First of all, we transform the bidirectional DiT into a causal autoregressive architecture by replacing full attention with block causal attention. Specifically, text tokens only attend to themselves, and visual tokens attend to text tokens and visual tokens of previous and current frames. Afterward, we change the model inputs. As illustrated in Fig. in addition to the regular noise and conditional inputs used by the original diffusion model, we change the model to also take in the past generated frame from the previous autoregressive step through channel concatenation, except the first autoregressive step where the input frame given by the user is used instead. During inference, our model runs autoregressively. At each autoregressive step, reuses the attention KV cache and generates the next frame in a single forward pass. The generated frame is recycled, along with new control condition, as inputs for the next autoregressive step.
To prevent the unbounded growth of attention computation and KV cache size, visual tokens attend to at most N past frames while always attending the text tokens and the first frame. It is worth noting that although each attention layer uses a window size of N, stacking multiple layers results in much larger effective receptive field.
Our architecture resembles that of large language models (LLMss), but with one important distinction: unlike conventional next-token prediction that outputs the token probabilities using a softmax layer,

---

our model generates all tokens for the next frame in single forward pass sampled by noise. In addition, our input recycling upproach is also more efficient than the one-step diffusion forcing, as shown in Fig. 2 Diffusion forcing is not optimized for the one-step generation scenario. When using KV cache, diftusion forcing requires computation on two frames on every autoregressive step, while ours only needs one.
2.2 Training Procedure
To create a one-step, per-frame, autoregressive generator, our training process involves three sequential stages: (1) diffusion adaptation, (2) consistency distillation, and (3) adversarial training.
Diffusion adaptation We load the pre-trained weights and finetune the model with the diffusion objective for architectural adaptation. We apply teacher-forcing training, where the ground-truth frames from the dataset are given as past-frame imputs. The output target is shifted by one frame to let the model perform next-frame prediction. Instead of pure noise, the noisy latent and the diffusion timestep t ~ (0 T) are still used per regular diffusion training. The same noise level is applied for all frames. This resembles LLMs training, where all the autoregressive steps trained in parallel.
Consistency distillation We apply consistency distillation
83
before adversarial training as an
initialization step to accelerate convergence following APT 49 Our modified formulation and architecture fully compatible with the original consistency distillation process without the need for modification. We omit classifier guidance (CFG) as we find it introduces artifacts in our autoregressive generation setting.
We extend APT 49 the autoregressive setting with improved discriminator design, training strategy, and loss objective.
Adversarial training
For the discriminator model, backbone, initialize from the diffusion weights post- cost-adaptttion, and insert logit output projection layers. We replace the noise input to frames and randomly sample timestep adaptation. A notable difference to APT discriminator design is that ours computes output logit for every frame instead of for the whole clip. This design naturally enables parallel multi-duration discrimination, as inspired by multi-resolution discrimination [39 38)
the same causal generator architecture as our discriminator
(1 7)) for fast
We find models trained with teacher-forcing incur significant error accumulation at inference. To address this, we introduce a student-forcing approach within the adversarial training framework. Specifically, the generator only uses the ground-trutt first frame and recycles the actual generated results as input for the next autoregressive step. In each training step. the generator is autoregressively invoked with KV cache to produce the video, exactly matching the inference behavior, while the discriminator evaluates all the generated frames in a single forward pass parallel. We find detaching the frss-frame input from the gradient graph improves stability. We allow the gradient flow through the KV cache to update all the parameters.
For the loss, than the non-saturating loss [8]. Specifically, we adopt the relativistic loss (36) and apply both the approximated I and R2 regularizations
R3GAN 31 objective our preliminary experiments find that it is more stable
773 65] as proposed in APT 49
Long- Video Training For the model to learn continuous generation of long videos, one must train it single-shot videos of long duration (e.g., 30-60 seconds). However, such long single-shot videos are rare in most training datasets, where the average shot duration is only 8 seconds. The lack of long-duration training leads to poor temporal extrapolation during inference.
To address the data limitation, we let the generator produce a long video, e.g. 60 seconds, and break it down into short segments, e.g. 10 seconds, for discriminator evaluation. We keep overlapping 1 --second duration for discriminator evaluation to encourage segment continuation. The discriminator trained generated segments and real videos from the dataset. This objective ensures that every segment of a generated long video fits the data distribution.
To fit the GPU memory, we also let the generator only produce segment at time to be evaluated by the discriminator. To produce the next segment, the generator reuses the detached KV cache from the last segment. The gradient is backpropagated after every segment evaluation for loss accumulation.
5

---

This technique can be used to train very long generators, with the trade-off of an increase in training time. We find this technique significantly improves the quality of long-duration video generation. This is made possible by the discriminator in adversarial training. Unlike supervised objectives that require ground-truth targets, the discriminator does not need explicit supervision for each input frame. Instead, it learns to distinguish real videos from generated ones. As a result, the model can learn from every video sample, rather than relying on a limited number of long-duration videos.
2.3 Interactive Generation Applications
We first train model for the general image-to-vided generation task without interactive conditions. This allows us evaluate the generation quality on standard benchmarks. We then train two separate models on the pose-conditioned human generation task and the camera-conditions world exploration task. This allows us to evaluate the controllability using two distinet condition signals. For the pose-conditioned human video generation task, we extract and encode the human pose from the the
training videos and provide it as a per-frame condition to the model following [46]. Similarly, for
world exploration generation task, we follow [ to extract and encode the camera origin and orientation as Plücker embeddings, with a few modifications to have better support causal generation. We use similar training datasets as used in these prior works 46 25 We
refer readers to our supplementary materials for additional details on our architecture, implementation, and training parameters.
3 Evaluation
Experimental Setups We use causal 3D convolution VAE [18] to compress the video temporally by 4 and spatially by 8. Therefore, our model autoregressively generates 4 video frames. The first input frame is independently compressed as a latent frame by the VAF. Since our VAE is causal, it naturally supports streaming decoding. We use attention window size N 30 attend to 30 latent frames (5 seconds). Additional details on the training setup are provided in the supplementary materials.
Following prior work 117.. we evaluate our method on the standard VBench- 12V benchmark (32) both 120-frame short-video generation and 1440-frame long-video generation. For comparison, we select Caus Vid [17].. Wan2. [95]. Hunyuan 44 MAGI-
Baseline and Metrics
75 SkyReel-Ve V2, and our own diffusion model baseline. These models are selected because Caus Vid is the state-of- the-art for fast streaming generation, and other models are available open-source video generation foundational models that support 12V. Note, CausVid is closed-source model and only reports VBench- -2V for 220-frame 12fps generation. Wan2.1 and Humyuan are bidirectional diffusion models that only support up to 120-frame generation. MAGI-1 and SkyReel- V2 are ceffuusion-forcinn models that support arbitrary-length streaming decoding, so we include them for the 1440-frams comparison. Our model is evaluated and compared at 736x 416 resolution. Additional inference settings and 1280 720 results are provided in the supplementary materials.
Main Results Figure 3 qualitatively compares our method on one-minute (1440-frame) video generation against SkyReel- V2, MAGI-1, and our diffusion baseline. All three of them exhibit strong error accumulation after 20 to 30 seconds. For our diffusion baseline, we experiment using lower CFG scale or using rescale [47] but it does not mitigate the exposure problem and can further cause more structural deformation, so we keep it at CFG 10. We also show that our AAPT model trained on only 10-second duration cannot generalize to long videos in Fig. 3d Long video training is critical, as shown in Fig. 3e Figure shows more results of our model across subjects and scenes.
Table I shows that our method achieves competitive performance compared to the state-of-the-at methods on the quantitative metrics. For 120-frame 12V generation, AAPT improves frame quality score and image conditioning scores compared to the diffusion baseline and is the best across all compared methods. The frame quality improvement concurs with the findings in APT (49 that adversarial training can improve visual quality. AAPT has resulted in slight decline in temporal quality score compared to the diffusion baseline, but is still above Wan and closely follows Hunyuan. We note that CausVid has exceptionally high temporal quality score, likely because it was trained on 12ps data, which usually results in a higher dynamic degree than other 24fps models, and the dynamic degree score is the main differentiator for the overall temporal quality. For 1440-frame

---

Os (Input)
10s
20s
30s
40s
50s
60s
(a) SkyReel-V2 (14B-540P Variant)
(b) MAGI-1 (24B-Distill Variant)
(c) Ours (Diffusion Extension)
(d) Ours (AAPT No Long Video Training)
(e) Ours (AAPT)
Figure 3: Qualitative comparison on one-minute, 1440-frame, VBench-122 generation.
Os (Input)
10s
20s
30s
40s
50s
60s
Figure 4: More results of our AAPT model for one-minute, 1440-frame, VBench-12V generation.
I2V generation, AAPT achieves the best quality scores across the comparison and has improved conditioning scores compared to the diffusion baseline. We note that SkyReel-V2 and MAGI-1 have higher image-conditioning score compared to our AAPT and diffusion baseline which is because most of the videos by MAGI-I are stationary. This reflected in much lower dynamic degree score and the qualitative visualization in Fig.
7

---

Table Quantitative comparisons on VBench-12V * denotes metrics that need special interpre- tation as discussed in the main text. The 6 quality metrics are aggregated as temporal quality and frame quality according to VBench- BBench-Competttion The best metrics are highlighted in bold.
Quality
Condition
Frames Method
Temporal Quality
Frame Quality
Subject Consistancy
Backgronnd Consistasey
Motion Smoothness
Dynamic Degree
Acshetic Quality
Imaging Quility
12V Suhject
12V
Bockground
Caus Vid
117
*92.00
65.00)
Not Reported
Wan 2.1
195
87.95
66.58
93.85
96.59
97.82
39.11
63.56
69.59
96.82
98.57
120
Hunyuan 44
89.80 64.18
93.06
95.29
98.53
54.80
60.58
67.78
97.71
97.97
Ours (Diffusion)
90.40 66.08
94.58
96.76
98.80
52.52
62.44
69.71
97.89
99. 14
Ours (AAPT)
89.51 66.58
96.22
96.66
99.19
42.44
62.09
71.06
98.60
99.36
SkyReel-Y
82.19 53.67
78,43
86.38
99.28
47.15
53.68
53.65
96.50
98.07
MAGI-1 775
80.79 60.01
82.23
89.27
98.54
25.45
52.26
67.75
*96.90
#98.13
1440
Ours (Diffusion)
86.65 60.49
82.38
89.48
98.29
66.26
56.46
64.51
95.01
97.72
Ours (AAPT)
89.79 62.16
87.15
89.74
99.11
76.50
56.77
67.55
96.11
97.52
Table 2: Quantitative comparison pose- conditioned human video generation task. Met- rics better than ours highlighted in bold.
Table 3:
Quantitative comparison camera- conditioned world exploration task. Metrics better than ours highlighted in bold.
Method
AKD] IQAt ASET FID FVD.
Method
FVD4 Movt Trans Rot,, Geot Apr
DisCo
9.313 3.707 2.396 57.12 64.52
MotionCtrl
221.23 102.21 0.3221 2.78 57.87 0.7431
Anyone
5.747 3.843 2.718 26.87
37.67
CameraCtrl
199.53 133.37 0.2812 2.81 52.12 0.7784
MimicMotion
8,536 3,977 2.842 23.43
22.97
CameraCtrl2
73.11 698.51 0.1527
1.58 88.70 0.8893
CyberHost
3.123 4.087 2,967 20.04
7.72
Ours (AAPT) 61.33 521.23 0.1185
1.63 81.25 0.9012
OmniHuman-1
2.136 4.111 2,986 19.50
7.32
Ours (AAPT)
2.740 4.077 2.973
22.43
11.78
Input
Generated
Control
Input
Generated
Figure 5: Pose-condee cossiconditi virtual human
Figure 6: Camera-controlled world exploration
Pose-Conditioned Human Video Generation We evaluate our method on post-conditioned human
video generation using the protocol and test set from previous work [45]. The pose accuracy
assessed via average keypoint distance (AKD) with keypoints extracted using DWPose [113]. For visual quality, use Q-Align [177, vision-language model, to evaluate image quality (IQA) and aesthetics (ASE). Additionally, Fréchet Inception Distance (FID) [27] and Frechet Video Distance (FVD) [91] measure the distributional alignment between the generated and the ground-truth samples. For comparison, we include four recent UNet- diffusion models, i.e. Disco [0I], AnimateAnyone (30 MimicMotion [22]] CyberHost 45), and the state-of-the
DiT-based OmniHuman- [46). Tableeppreeent the quantitative metrics. Among the six compared methods, ours is strong in pose accuracy and is ranked second only after the state-of-the of-the-art baseline OmniHuman-1. In terms of visual quality, ours consistently ranked second or third and is closely after CyberHost. Figure shows visualization of our method.
Camera-Condittoneed World Exploration We verify our method on the camera-comee cumera-conditioned world exploration task, also following the protocol of previous work [25]. We compute the FVD, movement strength (Mov), translational error (Trans), rotational error (Rot), geometric consistency (Geo), and appearance consistency (Apr). The details are provided in the supplementary materials. We compare
against previous state-of-the-are i.e. MotionCtrl [104), can CameraCtrl 1 & 2 [4 25). Table
shows that our method achieves new state-of-the-are in three out of six metries and closely follows CameraCtr12 for the rest.

---

Table 4: Latency and throughput comparison.
Inference speed We compare the throughput and latency of our method to other streaming
Method
Params H100 Resolution Latency FPS
video generation methods in Tab, 4 Our method significantly faster while achieving perfor- mance comparable to the state of the arts.
Cius
5B
1x
x40
4
1.30s
9.4
Ours
8B
1x
736x416
0.16s
24.8
MAGI-1
24B
8x
736
x416
8
7.00s
3,43
SkyReeIV2
14B
8x
x40
60
4.50s
0.89
4 Ablation Studies
Ours
8x
x280x720
0.17s
24.2
Long Video Training Table rreporss VBench-12Y V metrics on mod- els trained with different durations for one-minute video generation. Specifically, the model trained for 60s significantly outperforms the model trained for only 10s, showing the effectiveness of long video
Table 5: One-minute gener- ation performance using dif- ferent training durations.
training. Visualization is provided in Fig. 3d
Training Duration
Temporal Quality
Frame Quality
Teacher-Forcing and Student-Forcing Although diffusion adapta- tion and consistency distillation only support teacher forcing, adver-
10s
85,86
57.92
sarial training can be done in either teacher- sarcher-orccigg or student-forcing fashion. We describe the setup in the supplementary materials.
20s
85.60
65.69
60s
89.79
62.16
We find that models trained with teacher-forcing adversarial objective fail to generate proper videos at inference time, as shown in Figure 7 The content starts to drift significantly only a few frames into the generation process. Student-foreing training is critical in mitigating error accumulation. Although prior work has found that adding Gaussian noise to the input at training can reduce drifting at inference [93], it does not resolve the distribution gap from a fundamental level student-forcing training. We leave additional explorations to future works.
Limitations For consistency, our model can have difficulty maintaining the subject and the scene. This is caused by both the generator and the discriminator. Our generator adopts the ba- sic sliding window for simplicity. We leave the exploration of more architectures and op- timizations [105] 80 52 21 20] to future works. For the discriminator, current segment-basel based dis- crimination cannot enforce long-range consis- tency. This may be mitigated by adding identity embeddings [54] to the discriminator. For train- ing speed, the long video training process can be slow. For quality, we find that one-step generation can still create defects, and once the defects emerge, they can be kept in the scene for long time since the discriminator also enforces temporal consistency. More research needed to improve the quality of one-step generation. For the duration, we test model zero-shot five-minute generation. Our model can still generate content but with artifacts. We provide examples in the supplementary material.
Os (Input)
Is
2s
5s
Figure 7: Models trained with teacher-forcing ad- versarial objective fail to generate proper content at inference.
5 Conclusion
We have introduced autoregressive adversarial post-training (AAPT), method that uses adversarial training as a paradigm to transform video diffusion models into a fast autoregressive generator suitable for real-time interactive applications. Our model achieves performance comparable to that of the best methods while being significantly more efficient. We also analyze its limitations and aim to address them in future work.
Acknowledgment and Disclosure of Funding
We thank Weihao Ye for assistance with the evaluation. We thank Zuquan Song and Junru Zheng for assistance with the computing infrastructure. We thank Jianyi Wang and Zhijie Lin for their discussions during the work. This work is fully funded by ByteDance Seed.

---

References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.0877 088744, 2023.
[2) Eloi Alonso, Adam Jelley, Vincent Micheli, Anssi Kanervisto, Amos J Storkey, Tim Pearce, and François Fleuret. Diffusion for world modeling: Visual details matter in atari. Advances in Neural Information Processing Systems, 37:58757-58791, 2024.
[3] Tim Brooks, Janne Hellsten. Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming- Yu Liu, Alexei Efros, and Tero Karras. Generating long videos of dynamic scenes. Advances in Neural Information Processing Systems, 35:31769-31 117791 2022.
[4] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing. David Schnurr, Joe Taylor. Troy Luhman, Eric Luhman, al. Video generation models as world simulators. OpenAl Blog, 1:8, 2024.
[5] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020.
[6] Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holdes Yuge Shi, Edward Hughes. Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps. al. Genie: Generative interactive environments. Forty-first International Conference on Machine Learning, 2024.
[7] Boyuan Chen, Diego Martí Monsó, Yilun Du, Max Simchowitz, Russ Tedrake, and Vincent Sitzmann. Diffusion forcing: Next-token prediction meets full-sequence diffusion.. Advances in Neural Information Processing Systems, 00::24081-2425,, 2024.
[8] Dar- Yen Chen, Hmrishav Bandyopadhyay, Kai Zou, and Yi-Zhe Song. Nitrofusion: High-fidelity single-step diffusion through dynamic adversarial training. arXiv preprint arXiv:2412.020300 2024.
[9] Guibin Chen, Dixuan Lin, Jiangping Yang. Chunze Lin, Juncheng Zhu, Mingyuan Fan, Hao Zhang, Sheng Chen, Zheng Chen, Chengehen Ma, et al. Skyreels-v2: Infinite-lnngth film generative model. arXiv preprint arXiv:2504. 13074, 2025.
[10] Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling, arXiv preprint arXiv:2501. 17811, 2025.
[11] Zheng Chen, Zichen Zou, Kewei Zhang. Xiongfei Su, Xin Yuan, Yong Guo, and Yulun Zhang. Dove: Efficient one-step di diffusion model for real-world video super-resolution. arXiv preprint 2505 2025.
[12] Suhwan Cho, Minhyeok Lee, Seunghoon Lee, Chaewon Park, Donghyeong Kim, and Sangyoun Lee. Treating motion as option reduce motion dependency unsupervised video object segmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 5140-5149, 2023.
[13] Etched Decart, Quinn McIntyre, Spruce Camphell, Xinlei Chen, and Robert Wachen. Oasis: A universe in a transformer. URL: https://oasis-mode github. io, 2024.
[14] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024.
[15] FastHunyuan. https::/huugiingfceeeee http:/gggaaaateeeeee//eaa///aaae
[16] Ruili Feng, Han Zhang, Zhantao Yang, Jie Xiao, Zhilei Shu, Zhibeng Liu, Andy Zheng, Yukun Huang, Yu Liu, and Hongyang Zhang. The matrix: Infinite-hood horizon world generation with real-time moving control. arXiv preprint arXiv:2412.03356 2024.
[17] Yu Gao, Jiancheng Huang, Xiaopeng Sun, Zequn Jie. Yujie Zhong, and Lin Ma. Matten Video generation with manba-attention. arXiv preprini arXiv:2405.031 2024.
[18] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu. David Warde Farley Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
10

---

[19] Aaron Grattafiori. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, al. The llam 3 herd of models. arXiv preprint arXiv:4407.2188, 2024.
[20] Albert Gu and Tri Dao. Mamha: Linear-time sequence modeling with selective state spaces. arXiv preprint 22200221, 2023.
[21] Albert Gu, Karan Goel, and Christopher Ré. Efficiently modeling long sequences with structured state spaces, arXiv preprint 0rXiv2111.003900 2021.
[22] Junliang Guo, Yang Ye, Tianyu He, Haoyu Wu, Yushu Jiang, Tim Pearce, and Jiang Bian. Mineworld: a real-time and open-source interactive world model on minecraft. arXiv preprint arXiv:2544003388 2025.
[23] Yuwei Guo, Ceyuan Yang. Ziyan Yang, Zhibei Ma, Zhijie Lin, Zhenheng Yang, Dahua Lin, and Lu Jiang. Long context tuning for video generation. arXiv preprint arXiv:253. 10589, 2025.
[24] Hao Hc, Yinghao Xu, Yuwei Guo, Gordon Wetzstein, Bo Dai, Hongsheng Li, and Ceyuan Yang. Camer- actrl: Enabling camera control for text-to-video generation. arXiv preprint 2404.220000200 2024.
[25] Hao He, Ceyuan Yang, Shanchuan Lin, Yinghao Xu, Meng Wei, Liangke Gui, Qi Zhao, Gordon Wetzstein, Lu Jiang, and Hongsheng Li. Cameractrl ii: Dynamic scene exploration via camera-controlled video diffusion models. arXiv preprint arXiv:2503. 10592, 2025.
[26] Roberto Hensehel, Levon Khachatryan, Hayk Poghosyan, Daniil Hayrapetyan, Vahram Tadevosyan, Zhangyang Wang, Shant Navasardyan, and Humphrey Shi. Streamingt2v: Consistent, dynamic, and extendable long video generation from text. arXiv preprint arXiv:2403, 14773, 2024.
[27] Martin Heusel, Hubert Ramsauer. Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
[28] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
[29] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207112598. 2022.
[30] Li Hu. Animate anyone: Consistent and controllable image-to-video synthesis for character animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8153-8163, 2024.
[31] Nick Huang, Aaron Gokaslan, Volodymyr Kuleshov, and James Tompkin. The gan is dead; long live the gan! a modern gan baseline. Advances in Neural Information Processing Systems, 37:44177-44215, 2024.
[32] Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21807-21818, 2024.
[33] Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Shuaiwen Leon Song, Samyam Rajbhandari, and Yuxiong He. Deepspeed ulysses: System optimizations for enabling training of extreme long sequence transformer models. arXiv preprint Xr3: 14509, 2023. 2023.
[34] Jianwen Jiang, Chao I Liang, Jiaqi Yang, Gaojic Lin, Tianyun Zhong, and Yanbo Zheng. Loopy: Tam- ing audio-driven portrait avatar with long-term motion dependency. In The Thimeenth International Conference on Learning Representations, 2025.
[35] Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song, Yadong Mu, and Zhouchen Lin. Pyramidal flow matching for efficient video generative modeling, arXiv preprint arXiv:2410.05954 2024.
[36] Alexia Martttncan. The relativistic discriminator: a key element missing from standard gan. arXiv preprint arXiv:1807.00733 2018
[37] Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun- Yan Zhu, and Taesung Park. Distilling diffusion models into conditional gans. In European Conference Computer Vision, pages 428 447. Springer, 2024.

---

[38] Animesh Karnewar and Oliver Wang. Msg-gan: Multi-scale gradients for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and patiern recognition, pages 7799-
7808, 2020.
[39] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196. 2017.
[40] Jihwan Kim, Junoh Kang, Jinyoung Choi, and Bohyung Han. from text without training. arXiv preprint arXiv:2405. 11475, 2024.
Generating infinite videos
[41] Akio Kodaira, Chenfeng Xu, Toshiki Hazama, Takanori Yoshimoto, Kohei Ohno, Shogo Mitsuhori, Soichi Sugano, Hanying Cho, Zhijian Liu, and Kurt Keutzer. Streamdiffusion: A pipeline-lovel solution for real-time interactive generation. arXiv preprint arXiv:2312. 2023.
[42] Jonas Kohler, Albert Pumarola, Edgar Schönfeld, Artsiom Sanakoyeu, Roshan Sumbaly, Peter Vajda, and Ali Thabet. Imagine flash: Accelerating emu diffusion models with hackward distillation. arXiv preprint arXiv:2405.05224 2024.
[43] Dan Kondratyuk, Lijun Yu, Xiuye Gu, José Lezama, Jonathan Huang, Grant Schindler, Rachel Hornung. Vighnesh Birodkar, Jimmy Yan, Ming-Chang Chiu, et al. Videopoet: A large language model for zero-shot video generation. arXiv preprint arXiv:2312.14123 2023.
[44] Weijie Kong, Qi Tian, Zijian Zhang. Rox Min, Zuozhuo Dai, Jin Zhou, Jiangleng Xiong, Xin Li, Bo Wu, Jianwei Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603 2024.
[45] Gaojie Lin, Jianwen Jiang. Chao Liang. Tianyun Zhong, Jiaqi Yang, Zerong Zheng, and Yanbo Zheng Cyberhost: A one-stage diffusion framework for audio-driven talking body generation. In The Thirteenth International Conference on Learning Representations, 2025.
[46] Gaojie Lin, Jianwen Jiang, Jiaqi Yang. Zerong Zheng. and Chao Liang. Omnihuman- 1: Rethinking the scaling-up up stags conditioned human animation models. arXiv preprint arXiv:2502.0100 2025.
[47] Shanchuan Lin, Bingchen Liu, Jiashi Li. and Xiao Yang. Common diffusion noise schedules and sample steps are flawed. In Proceedings of the IEEE/CVF winter conference applications of computer vision, pages 5404-5410 2024.
[48] Shanchuan Lin, Anran Wang, and Xiao Yang. Sexl-lightning Progressive adversarial diffusion distillation. arXiv preprint arXiv:2402. 2at.:2202./3999 /3929, 2024.
[49] Shanchuan Lin, Xin Xia, Yuxi Ren, Ceyuan Yang, Xuefeng Xiao, and Lu Jiang. Diffusion adversarial post-training for one-step video generation. arXiv preprint arXiv:2501.083/1 2025.
[50] Shanchuan Lin and Xiao Yang. Animatediff-lgghning Cross-model diffusion distillation. arXiv preprint
arXiv:2403. /2706, 2024.
[51] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le.. Flow matching for generative modeling. arXiv preprint 20000:2210002777, 2022.
[52] Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang. Chong Ruan, al. Deepseck-v3 technical report. arXiv preprint arXiv:2412.194 19437, 2024.
[53] Feng Liu, Shiwei Zhang, Xiaofeng Wang. Yujie Wei, Haonan Qiu, Yuzhong Zhao, Yingya Zhang, Qixiang Ye, and Fang Wan. Timestep embedding tells: It's time to cache for video diffusion model. arXiv preprint arXiv:2411.19100 2024.
[54] Lijie Liu, Tianxiang Ma. Bingchuan Li. Zhuowei Chen, Jiawei Liu, Qian He, and Xinglong Wu. Phantom: Subject-consistent video generation via cross-modal alignment. arXiv preprint vrXiv:2502.11079 2025.
[55] Xingchao Liu, Chengyue Gong, and Qiang Liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.0300 03003. 2022.
[56] Xingchao Liu, Xiwen Zhang, Jianzhu Ma. Jian Peng. et al. Instaflow: One step is enough for high- quality diffusion--asse based text-to-image generation. In The Twelfth International Conference on Learning Representations, 2023.
[57] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint axXiv:171..051002200 2017.
12

---

[58] Cheng Lu and Yang Song, Simplitying, stabilizing and scaling continuous-time consistency models. arXiv preprint arXiv:2410.1088/ 2024.
[59] Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. Advances in Neural Information Processing Systems, 35:5775-5787, 2022.
[60] Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models. arXiv preprint arXiv:2211.01095, 2022.
[61] Weijian Luo, Tianyang Hu, Shifeng Zhang. Jiacheng Sun, Zhenguo Li, and Zhihua Zhang. Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models. Advances in Neural Information Processing Systems, 36:76525 76546, 2023.
[62] Yuxuan Luo, Zhengkun Rong, Lizhen Wang, Longhao Zhang, Tianshu Hu, and Yongming Zhu. Dreamactor-ml: Holistic, expressive and robust human image animation with hybrid guidance. arXiv preprint (ix.xi2254000722 2025.
[63] Xinyin Ma, Gongfan Fang. Michael Bi Mi, and Xinchao Wang. Learning-to-cacte Accelerating diffusion transformer via layer caching. Advances in Neurul Information Processing Systems, 37:133282-133304 2024.
[64] Xiaofeng Mao, Zhengkai Jiang, Fu- Wang. Jiangning Zhang. Hao Chen, Mingmin Chi, Yabiao Wang. and Wenhan Luo. Osv: One step is enough for high-quality image to video generation. arXiv preprini arXiv:2409.113 11367, 2024.
[65] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International conference on machine learning, pages 3481-3490. PMLR, 2018.
[66] Shentong Mo and Yapeng Tian. Scaling diffusion mamba with bidirectional ssms for efficient image and video generation. arXiv preprint arXiv:2405. 15881, 2024.
[67] Jack Porker-Holder, Philip Ball, Jake Bruce. Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis. Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi, Stephen Spencer, Jessica Yung. Michael Dennis, Sultan Kenjeyev, Shangbang Long, Vlad Mnih, Harris Chan. Maxime Gazeau, Bonnie Li, Fabio Pardo, Luyu Wang. Lei Zhang, Frederic Besse, Tim Harley, Anna Mitenkova, Jane Wang. Jeff Clune, Demis Hassabis, Raia Hadsell, Adrian Bolton, Satinder Singh, and Tim Rocktäschel. Genie 2: A large-scale foundation world model. 2024.
[68] William Peebles and Saining Xic. Scalable diffusion models with transformers. In Proceedings of the 2023.
IEEE/CVF international conference on computer vision, pages
[69] Adam Polyak, Amit Zohar, Andrew Brown, Andros Tjandra, Animesh Sinha. Ann Lee, Apoorv Vyas, Bowen Shi, Chih- Yao Ma, Ching Yao Chuang, et al. Movie gen: cast of media foundation models. arXiv preprint arXiv:2410.1311
2024.
[70] Alec Radford, Jong Wook Kim. Chris Hallacy. Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PmLR, 2021.
[71] Shuhuai Ren, Shuming Ma, Xu Sun, and Furu Wei. Next block prediction: Video generation via semr-auto modeling. arXiv preprint arXiv:2502.0772 2025.
[72] Yuxi Ren, Xin Xia, Yanzuo Lu, Jiacheng Zhang, Jie Wu, Pan Xie, Xing Wang, and Xuefeng Xiao. Hyper-sd: Trajectory segmented consistency model for efficient image synthesis. arXiv preprint arXiv:2404. 13686, 2024.
[73] Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann. Stabilizing training of generative adversarial networks through regularization. Advances in neural information processing systems, 30, 2017.
[74] Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models, arXiv preprint 22t.:22002.00112. 2022.
[75] Sand-AI. Magi-1: Autoregressive video generation scale, 2025.
[76] Axel Sauer, Frederic Bocsel, Tim Dockhorn, Andreas Blattmann, Patrick Esser, and Robin Rombach. Fast high-resolution image synthesis with latent adversarial diffusion distillation. In SIGGRAPH Asia 2024 Conference Papers, pages 1-11,2024 2024.
13

---

[77] Axel Sauer, Dominik Lorenz, Andreas Blatimann, and Robin Rombach. Adversarial diffusion distillation. In European Conference on Computer Vision, pages 87-103. Springer, 2024.
[78] Team Seawead, Ceyuan Yang. Zhijie Lin, Yang Zhao, Shanchuan Lin, Zhibei Ma. Haoyuan Guo, Hao Chen, Lu Qi. Sen Wang. et al. Seaweed-7b: Cost-effective training of video generation foundation model. arXiv preprint arXiv:2504.0868 2025.
[79] Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, and Tri Dao. Flashattention- Fast and accurate attention with asynchrony and low-precision. Advances in Neural Information Processing Systems, 37:68658 68685, 2024.
[80] Noam Shazeer. Fast transformer decoding: One write-head is all you need. arXiv preprint 1rXiv:99110021550 2019.
[81] Ivan Skorokhodov, Sergey Tulyakov, and Mohamed Elhoseiny. Stylegan-v: A continuous video generator with the price, image quality and perks of stylegan2. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pages 3626-3636, 2022.
[82] Yang Song and Prafulla Dhariwal. Improved techniques for training consistency models. arXiv preprint arXiv:2310. 14189, 2023.
[83] Yang Song, Prafulla Dhariwal, Mark Chen, and llya Sutskever. Consistency models. 2023.
[84] Yang Song, Jaseha Sohl Sohk-DDekstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint 1rXiv:2011.1344 2020.
[85] Michal Stypulkowski, Konstantinos Vougioukas. Sen He, Maciej Zięba, Stavros Petridis, and Maja Pantic. Diffused heads: Diffusion models beat gans on talking-face generation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 5091-5100, 2024.
[86] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding, Neurocomputing, 127063,, 2024.
[87] Chameleon Team. Chameleon: Mixed-modal carly-fusion foundation models. arXiv preprint
09818 2024.
[88] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In Computer Vision ECCV 2020: 16th European Conference, Glasgow, UK, August 23 28, 2020, Proceedings, Part II 16. pages 402-404 419. Springer, 2020.
[89] Linrui Tian, Qi Wang, Bang Zhang, and Liefeng Bo. Emo: Emote portrait alive generating expressive portrait vidcos with audio2video diffusion model under weak conditions. In European Conference on Computer Vision, pages 244-260. Springer, 2024.
[90] Ye Tian, Xin Xia, Yuxi Ren, Shanchuan Lin, Xing Wang. Xuefeng Xiao, Yunhai Tong, Ling Yang, and Bin Cui. Training-free diffusion acceleration with bottleneck sampling. arXiv preprint arXiv:2503. 18940, 2025.
[91] Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphačl Marinier, Marcin Michalski, and Sylvain Gelly. Fvd: A new metric for video generation.
[92] Thomas Unterthiner, Sjoerd Van Steenkiste, Karol Kurach, Raphael Marinier, Marein Michalski, and Sylvain Gelly. Towards accurate generative models of video: A new metric challenges. arXiv preprint 11Xiv:1812.01717, 2018
[93] Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. Diffusion models are real-time game engines. arXiv preprint arXiv:2408. 14837, 2024.
[94] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[95] Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen- Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. Jianyuan Zeng. et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314 2025.
[96] Fu- Yun Wang, Wenshuo Chen, Guanglu Song, Han-Jia Jia Ye, Yu Liu, and Hongsheng Li. Gen-I-video: Multi-text to long video generation via temporal co- co-denoising, arXiv preprint (tl.:330..18644 2023.
14

---

[97] Fu- Yu- Wang. Zhaoyang Huang, Weikang Bian, Xiaoyu Shi. Keqjang Sun, Guanglu Song, Yu Liu, and Hongsheng Li. Animatelem: Computation-efficcen personalized style video generation without personalized video data. In SIGGRAPH Asia 2024 Technicul Communications, pages 1-5. 2024.
[98] Hongjie Wang. Yaih-Yao Ma, Yen-Cheng Liu, Ji Hou, Tao Xu, Jialiang Wang, Felix Juefei-Xu, Yaqiao Luo, Peizhao Zhang. Tingho Hou, et al. Lingen: Towards high-resolution minute-length text-to-video generation with linear computational complexity. arXiv preprint arXiv:2412.008830 2024.
[99] Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, et al. Seedvr2: One-step video restoration via diftusion adversarial post-training. arXiv preprint arXiv:2506.00 05300, 2025.
[100] Jianyuan Wang, Nikita Karaev, Christian Rupprecht. and David Novotny. Vggsfi:: Visual geometry grounded deep structure from motion. In Proceedings the IEEE/CVF conference computer vision and pattern recognition, pages (1686-26697, 2024.
[101] Tan Wang, Linjie Li, Kevin Lin, Yuanhao Zhai, Chung-Ching Lin, Zhengyuan Yang. Hanwang Zhang, Zicheng Liu, and Lijuan Wang. Disco: Disentangled control realistic human dance generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9326-93366 2024.
[102] Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang. Fan Zhang, Yueze Wang. Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. arXiv preprint arXiv:2409. 18869, 2024.
[103] Yuqing Wang. Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, and Xihui Liu. Parallelized autoregressive visual generation. arXiv preprint arXiv:2412.151 19, 2024.
[104] Zhouxia Wang. Ziyang Yuan, Xintao Wang, Yaowei Li, Tianshui Chen, Menghan Xia, Ping Luo, and Ying Shan. Motionctrl: A unified and flexible motion controller for video generation. In ACM SIGGRAPH 2024 Conference Papers, pages 1-11, 2024.
[105] Jeffrey Willette Heejun Lee, Youngwan Lee, Myeongjae Jeon, and Sung Ju Hwang. Training-free exponential context extension via cascading kv cache. arXiv preprint arXiv:2406. 17808, 2024.
[106] Chengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan, al. Janus: Decoupling visual encoding for unified multimodal understanding and generation. arXiv preprint vrXiv:2410.18444 2024.
[107] Haoning Wu, Zicheng Zhang. Weixia Zhang, Chaofeng Chen, Liang Liao, Chunyi Li, Yixuan Gao, Annan Wang, Erli Zhang, Wenxiu Sun, al. Q-align: Teaching Imms for visual scoring via discrete text-defined levels. arXiv preprint arXiv:2312 17090, 2023.
[108] Desai Xie. Zhan Xu, Yicong Hong. Hao Tan, Difan Liu, Feng Liu, Arie Kaufman, and Yang Zhou. Progressive autoregressive video diffusion models. arXiv preprint arXiv:2410.08151 2024.
[109] Enze Xie, Junsong Chen, Junyu Chen, Han Cai, Haotian Tang, Yujun Lin, Zhekai Zhang, Muyang Li, Ligeng Zhu, Yao Lu, et al. Sana: Efficient high-resolution image synthesis with linear diffusion transformers. arXiv preprint arXiv:2410. 10629, 2024.
[110] Yanwu Xu, Yang Zhao, Zhisheng Xiao, and Tingbo Hou. Ufogen: You forward once large scale text-to- image generation via diffusion gans. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 8196-8206, 2024.
[111] Hanshu Yan, Jun Hao Liew. Long Mai, Shanchuan Lin, and Jiashi Feng. Magicprop: Diffusion-baase based video editing via mowirn- appearance propagation. arXiv preprint arXiv:2309.00 00908, 2023.
[112] Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, and Jiashi Feng. Perflow: Piecewise rectified flow as universal plug-and-play accelerator. arXiv preprint 24X0::2000.07100 2024.
[113] Zhendong Yang, Ailing Zeng, Chun Yuan, and Yu Li. Effective whole-body pose estimation with two- stages distillation. In Proceedings of the IEFF/CVF International Conference on Computer Vision, pages 4210-4220, 2023.
[114] Yang Ye, Junliang Guo, Haoyu Wu, Tianyu He, Tim Pearce, Tabish Rashid, Katja Hofmann, and Jiang Bian. Fast autoregressive video generation with diagonal decoding. arXiv preprint arXiv:2503.1407 14070. 2025.
15

---

[115] Tianwei Yin, Michaël Gharbi, Taesung Park. Richard Zhang, Eli Shechtman, Fredo Durand, and Bill Freeman. Improved distribution matching distillation for fast image synthesis. Advances neural information processing systems, 37:47455- 47487, 2024.
[116] Tianwei Yin, Michaël Charbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. Proceedings of the IEEE/CVF conference on computer vision and paitern recognition, pages 6613-6623, 2024.
[117] Tianwei Yin, Qiang Zhang, Richard Zhang, William T Freeman, Fredo Durand, Eli Shechtman, and Xun Huang. From slow bidirectional to fast causal video generators. arXiv preprint arXiv:2412.0777.. 2024.
[118] Lijun Yu, José Lezama, Nitesh B Gundavarapu, Luca Versari, Kihyuk Sohn, David Minnen, Yong Cheng. Vighnesh Birodkar, Agrim Gupta, Xiuye Gu, et al. Language model beats diffusion-tokenizze is key to visual generation. arXiv preprint 23 2023.
[119] Jintao Zhang, Haofeng Huang. Pengle Zhang. Jia Wei, Jun Zhu, and Jianfei Chen. Sageattention2 technical report: Accurate 4 bit attention for huu-and--lay inference acceleration. arXiv preprint arXiv:2411.10955 2024.
[120] Jintao Zhang, Haofeng Huang, Pengle Zhang, Jun Zhu, Jianfei Chen, et al. Sagcattention: Accurate 8-bit attention for plug-and aad--nnd-plyy -lay inference acceleration. arXiv preprint arXiv:2410.02367, 2024.
[121] Peiyuan Zhang. Yongqi Chen, Runlong Su. Hangliang Ding, lon Stoica, Zhenghong Liu, and Hao Zhang. Fast vidco generation with sliding tile attention. arXiv preprint arXiv:2502.04507 2025.
[122] Yuang Zhang, Jiaxi Gu, Li- Lin Wang, Han Wang. Junqi Cheng. Yuefeng Zhu, and Fangyuan Zou. Mimicmotion: High-qualit human motion video generation with confidence-aware pose guidance. arXiv preprint arXiv:2406. 19680, 2024.
[123] Zhixing Zhang, Yanyu Li, Yushu Wu, Anil Kag, Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin, Junli Cao, Dimitris Metaxas. Sergey Tulyakov, et Sf-v: Single forward vidco generation model. Advances in Neural Information Processing Systems, 37:103399-10036 2024.
[124] Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, et al. Pytorch fsdp: experiences on scaling fully sharded data parallel. arXiv preprint arXiv:2304.11 1277, 2023.
[125] Chang Zou, Xuyang Liu, Ting Liu, Siteng Huang, and Linfeng Zhang. Accelerating diffusion transformers with tosen-wise feature caching. arXiv preprint 17. 2024.
16

---

A Model Architecture
Diffusion Transformer Our diffusion transformer largely follows the MMDiT design 14 It has 8B parameters and 36 transformer blocks. The discriminator adopts the same architecture. Therefore, our generator and our discriminator consist of 16B parameters for the adversarial training.
Block Causal Attention We implement block causal attention using Flash Attention 79 a for-loop. We find to provide reasonable performance for training. We leave the exploration for more performance implementation to future work. For inference, recurrent autoregressive steps are taken, and Flash Attention 3 can be naturally adopted without performance penalty.
Positional Embedding As the duration of the generation becomes agnostic our causal architecture, we modify the 3D rotary positional embeddings (RoPE) [86). Specifically, the positional embeddings continue to stretch dynamically along the spatial dimension to help the model generalize to different resolutions, while the positional embeddings are changed to have fixed interval along the temporal dimension to support arbitrary lengths of training and generation.
We adopt FSDP T24 for data parallelism. We ZERO 2 for the generator during student- forcing training that requires recurrent forward calls to avoid repeated parameter gathering, and ZERO for all other modules to save memory. We also adopt Ulysses [B3] as our context parallel strategy. We shard each video sample across 8 GPUs. Gradient checkpointing is also utilized per transformer block to fit the memory requirement.
Parallelism
B Training Details
After changing the architecture to block causal attention and adding the recycled input channels, we first adapt the model with diffusion training.
Diffusion Adaptation
51 Specifically, given sample zo and noise c, input is derived through linear interpolation x; = (1 - t) 2o + t The diffusion timestep is sampled uniformly t ~ (4, 1). then passed through a shifting function shift (t, s) == (s x t)/(1 + (s 1) x t). where = 24. Note that the same timestep is used for the entire clip without the diffusion-forcing ? approach of assigning independent timesteps for each frame. Our model predicts the velocity e with the mean squared error loss. We apply the teacher-forcing paradigm and provide the ground-truth frames without noise as recycled input. The noisy input and the output target are shifted by one frame to facilitate next frame prediction.
We follow the original model to use the flow motthhg parameterization
zo and penalized
We use AdamW optimizer 57 with a learning rate of le-5 and weight decay scale of 0.01 throughout the process. We first train on 736x416 (equivalent to 640x by area) 5-second videos for 20k iterations with batch size of 256. Then, add 1280x 720 to the mix for another 6k iterations with a batch size 128. Finally, we turn up the maximum duration of 416 resolution vidcos to 15 seconds for 4k itcrations with a batch size of 32. This curriculum allows our model to see enough samples in the carly stages and see longer samples in the final stage.
Consistency Distillation Then we apply consistency distillation though the results after consistency distillation are blurry, provides better initialization for the adversarial training stage, as discovered by APT [49
S
create a one-step generator. Al-
We inherit the same AdamW settings and the datasct settings as in the last diffusion adaptation stage. We distill the model on 32 fixed steps, which are uniformly selected and then passed through the shifting function with a shifting factor = 21. We do not apply fassifier-frre guidance (29. We continue to use the teacher-foreing paradigm provide ground-truth frames as recycled inputs, and shift the noisy inputs and output targets by one frame following the diffussin adaptation approach. We follow the improved consistency distillation technique [2) and do not apply exponential moving average on the consistency target. No additional modification is needed for consistency distillation. The model is trained for 5k iterations.
Adversarial Training Finally, we perform adversarial training. In this stage, switch to the student- forcing paradigm. where the generator only takes the first frame as input and recycles the actual generated frame for the next autoregressive step, strictly following inference procedure. Then, the discriminator evaluates the generated results in parallel, producing logits after each frame for multi-duration discrimination.
We follow APT (4 to initialize the generator from the consistency distillation weights, and to initialize the discriminator from the diffusion adaptation weight. We change to use the relativistic pairing loss (3)
LAGGAN e) F(D(G(e,e),,) ()((o,)))))(((ooc))
(1)
17

---

where G.D denote the generator and the discriminator respectively, Sa(=) log(1 + = fn(x)
log(1 e²) is used each of their update steps respectively, c denotes the text condition and other interactive
conditions. We calculate R! and R2 regularization 73 65 through the approximation technique proposed in
APT 49
LARI ((oo ((N e)).
(2)
(3) where 0.1 and À 1000. Since the discriminator is initialized from the difflusion model, follow APT to provide timesteps by random uniform sampling (4, We do not shift the timestep for the discriminator. We use RMSProp optimizer with a = 0.9 following APT [49
R2 = XID(G((,O),) c))
We first perform training without the long-video extension training technique. The vidcos are 5s to 10s in duration. We train it using a low learning rate of 3e-6 following APT (49 and batch size of 256 for 500 generator updates. The resulting model can only generate up to 10 seconds and will drift for videos longer than 10 seconds.
Then we apply the long video training technique. The training videos are still from 5s to 10s, and extend once with an overlap Is to a total maximum duration of 19s (10 + (10-1)). This stage is trained 500 generator updates. Then we turn up the extension to times, to a total maximum duration of 55s (10 + 5x(10-1)),
We find it necessary to decrease the batch size to 64 and increase the learning rate to le-5 for the extension training for the model make adequate changes in reasonable amount of time.
Since the generator in student-forcing mode must recurrentl perform model forward for each autoregressive step during training, we switch FSDP ZERO 2 mode to save the model parameters on each machine. This avoids repeated parameter gathering and improves the training sced. The discriminator and text encoder still adopt ZERO to shard all the model parameters for memory saving.
Computational Resources We 256 H100 GPUs for final training and employ gradient accumulation where necessary to reach our final batch size. The model is trained approximately days, where the diffusion adaptation and the long-video adversarial training take the majority the time.
C Variational Autoencoder
We train a lightweight VAE decoder to fit the real-time budget. Specifically, original VAE decoder has 3 residual blocks per resolution scale, and has channels [128, 256, 512, 512] at each resolution scale. Our lightweight VAE decoder reduces number residual blocks per resolution to 2, and reduces the channels to [64, 128, 256, 512]. This results in nearly 3 times speed-up without visible quality degradation.
D Teacher-Foreing Adversarial Training
The adversarial training supports both student-forcing and leacher-forcing modes. To implement student forcing, the generator runs autoregressively with KV cache and recycles the actual generated frame as imput for the next autoregressive step. The discriminator evaluates the results in parallel. To implement teacher forcing, the generator takes ground- ground-truth video frames as past prediction inputs and predicts the next frames in parallel. The discriminator runs autoregressively and always uses the KV cache from the real videos to attend to the ground-truth past frames.
Figure X visualizes teacher-foreing adversarial train- ing. Specifically, in teacher-foreing mode, the gen- erator given input 11, 12, 13 generates independent output O2, O3, O4. Namely, the output O3 only has correlation with 12 but not with O2. Therefore, the discriminator must independently evaluate the generated results with their correct dependencics to produce logits L2, L3, L4. Since the discriminator transformer is causal, the repeated computation can be saved using KV cache.
Generator
Diseriminator
05
Oi
12
B3
O2
12
03
II
I
Q4
Figure 8: feacher-forcing adversarial training
We have conducted experiments with teacher-forcing adversarial training, and the model fails to generate reasonable videos as discussed the main paper. We suspect LLMs are able to train with macher-forcing mode because they use discrete codebook to encode words, where slight inaccuracy is less relevant. But our model predicts continuous latent values for the entire frame, where slight inaccuracy accumulates.
18

---

E The Importance of Result Recycling
We conduct an experiment to study the importance of result recycling. Specifically, we keep the exact architecture and training settings, and we mask the recycled input as zero tensors, except the first frame, which takes in the user image. We find that models trained without recycling input cannot generate large motion. Some of the movements become incohesive well. The video visualization is provided on our wehsite.
F 12V Evaluation
The table in the main text compares our model under the 736x416 setting. For the other models we compare to, we largely follow the default sampling setting for each model, including the number of steps and CFG [2] We also use the default resolution for each model to ensure that the model has been properly trained on the expected resolution. Specifically, we use 896x 544 for Hunyuan [4]] 832x for Wan2.1 [95]. 960x 544 for SkyReel- V2 (9.. We note that we run 5 samples per prompt for all the comparisons per VBench-12V (32
requirement, except for SkyReel-V- V2 which only I sample per prompt and reduce the sampling steps from its default 50 to 30. This is because SkyReel-V2 too computationally intensive to generate one-minute videos.
We additionally provide the evaluation metrics under the x280x720 resolution in Tab. 6 Note that x280x trained and inference with a smaller attention window size N 15 to the memory.
Table 6: Quantitative VBench-12V 32 metrics on 1280x720 compared to 736x416.
Quality
Condition
Frames
Method Resolution
Temporal Quality
Frame Quality
Background Consistency
Motion
Dynamic Degree
Aesthetic
Imaging Quality
12V
12V
Consistency
Smoothness
Quality
Subject
Background
416 1280x 720
89.79 62. 16
87.15
89.74
99. 19.11
76.50
56.77
67.55
11
97.52
1440 Ours
88.24 64.30
87.95
90.10
99.16
63.29
57.79
70.80
96.51
98.18
G
Camera-Conditionel World Exploration
Training We make a few modifications on CameraCtrl 2 to make it hetter support causal generation. First, CameraCtrl II uses Plücker embeddings to represent the camera position and orientation, where it treats the first frame the initial position, and the other frames are relative to the first frame. This is problematic as the value can grow unbounded if the displacement forever increases. We change it that each frame only relative to the previous frame. Hence, the Plücker embeddings only represent the camera changes between immeddate frames to prevent unbounded growth of values. Second, CameraCtrl II uses the original Plücker coordinate to represent cach camera ray, which consists of a direction vector and moment vector. The moment vector encodes the displacement information, which is computed as the cross product of point on the line and the direction vector. We find that this implicit representation unnecessarily increases the complexity for the model to learn. Rather, we directly encode the camera ray's 'ss origin and direction. Third, the input scaling to the model in fact a hyperparameter that is not previously explored. We scale the coordinate inputs to roughly I standard deviation to simplify model learning. We also drop samples whose camera embeddings have very large values. These outliers caused by inaccurate camera estimation and detrimental the stability of adversarial training. Last, we use random initialization instead zero initialization for input projection of the new channels. We find that random initialization helps the model to adapt to the new inputs much more quickly.
The camera- cunera-conditioned model is trained separately from the 12V model. We start from the I2V diffusion adaptation weights and continue training on the camera-conditionas task. The consistency distillation and adversarial training are done separately for this dedicated model. The training settings mostly the same as the 12V model. For the long-video extension training, randomly sample new camera trajectories for the extended parts.
Evaluation Our evaluation metrics follow CameraCtrl 25 Specifically, we compute Fréchet Vidco Distance (FVD) [2 against the ground-truth videos. We compute the movement strength (Mov) on RAFT- extracted [8 dense optical flow of foreground objects identified by TMO-generated [[2] segmentation masks. Translational (Trans) and rotational (Rot) errors computed by comparing estimated camera parameters using VGGSIM 100 with the ground truth. Geometric Consistency (Geo) computed as the successful ratio of VGGSIM to estimate camera parameters. This indicates the quality of 3D geometry consistency of the generated scene. Appearance Consistency (Apr) is computed by comparing the cosine distance of each frame's CLIP 77 vision embedding to the average of the entire video clip.
19

---

H
Societal Impacts
Our work proposes new approach for real-time streaming video generation for interactive applications. Our approach is faster and more computationally efficient than existing approaches. This potentially enables the adoption of more real-time interactive applications. We do not consider our work to bring risk for significant negative socictal impacts. The vidcos generated by our method still contain imperfections that are casy to identify as generated vidcos, which prevents the technology from being used for malicious purposes.
20

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Yes, main claims in the abstract and introduction accurately reflect the paper's contributions and scope.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be pereeived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals motivation long it is clear that these goals attained by the paper.
2. Limitations
Question: Does paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Yes, a limitation section is provided toward end of the paper.
Guidelines:
The answer NA means that paper has no limitation while answer means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust results are violations these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect the scope of the claims made, e.g., if the approach was only tested a few datasets with few runs. In general, empirical results often depend implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
grounds for rejection,
Theory assumptions and proofs
Question: For each thcoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper does not include theoretical results.
Guidelincs:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
21

---

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors encouraged to provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Answer: [Yes]
Justification: The details of our architecture, implementation, hyperparameters, training procedures, and evaluation setups provided in supplementary materials for reproducibility.
Guidelines:
The answer NA means that the paper does not include experiments.
the paper includes experiments, No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example. if the contribution is novel architecture, describing the architecture fully might suffice, if the contribution is specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to hosted model (e.g., in the case of a large language model), releasing of model checkpoint, or other means that appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submissions provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (e.g.. a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g.. with open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, which case authors are welcome to describe the particular way they provide for reprodueibility. In the case of closed-source source models, it may be that access to the model is limited in some way (c.g.. registered users), but it should be possible for other rescarchers have some path reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code. with sufficient instructions to faithfully reproduce the main experimental results, described in supplemental material?
Answer: [No]
Justification: The model, data, and code used in this paper is proprictary and not currently scheduled for release. However, we describe our methodology in details for reproducibility.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines
http:///iipscccppuiiiee
Criides/CodeSule iondeuuubnissiooooooooy. for more details.
While we encourage release of code and data, we understand that this might not possible,
so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g.. for a new open-soure benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https: : //nnis//......///// cc/public/ qul.dess/Coodeuubmmeessoooooo... for more details.
22

---

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reprodueible, they should state which ones are omitted from script and why.
At submission time, to preserve anonymity, authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code is permitted.
Experimental setting/details
Question: Does the paper specify all the training and test details (e.g.. data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Answer: [Yes]
Justification:
The details are provided supplementary materials.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined other appropriate informa- tion about the statistical significance of the experiments?
Answer: [No]
Justification: We follow established benchmarks and the evaluation protocols of prior works, which do not report error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some paramcter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures
symmetric error hars that would yield results that are out of range (c.g. negative error rates).
error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
Experiments compute resources
Question: For each experiment, does the paper provide sufficient information the computer resources (type of compute workers, memory, time exccution) needed reproduce the experiments?
Answer: [Yes]
Justification:
We report the resources in supplementary materials.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
23

---

The paper should provide the amount of compute required for each of the individual experimental well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn 't make into the paper).
9. Code of ethics
Question: Does the research conducted the paper conform, every respect, with NeurIPS Code of Ethics
Answer:
[Yes]
Yes, the research in the paper fully conforms to the NeurIPS Code of Ethics in every respect.
Justification:
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure preserve anonymity (e.g., if there is special consideration due laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts the work performed?
Answer: [Yes]
Yes. we analyze the potential social impact supplementary material due to space limitations.
Justification:
Guidelines:
The answer NA means that there is no societal impact of the work performed.
the authors answer NA or No, they should explain why their work has no socictal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g.. disinformation, generating fake profiles, surveillance), fairness considerations (c.g., deploy- ment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it not needed point out that generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitor- ing misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of MI.).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g.. pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification:
The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines restrictions access the model implementing safety filters.
24

---

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but encourage authors take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification:
The assets used properly credited and licensed.
Guidelines:
The answer NA means that the paper does not existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include a URL.
The name of the license (c.g., CC-BY 4.0) should be included for cach asset. scraped from particular source (e.g.. website), the copyright terms of service of that source should provided.
assets are released, license, copyright information, and terms of use the package should be provided. For popular datasets, paperswi thcode. www.coom//aaac /attttttstts has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.
For existing datasets that are re-per re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
this information not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the datasct/code/model as part of their sub- missions via structured templates. This includes details about training, license, limitations,
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an amonymized URL or include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, well as details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsoureing nor research with human subjects.
Guidelincs:
The answer NA means that the paper does not involve crowdsourcing research with human subjects.
Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should included the main paper.
According to the NeurIPS Code of Fthics, workers involved data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Question:
25

---

Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, approval (or equivalent) may be required for any human subjects research. If you ohtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applica- ble), such the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe usage LLMs it an important. original. or non-standard component the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the rescarch, declaration is not required.
Answer: [NA]
Justification: The core method development in this research does not involve LLMs as any important, original, non-standard components.
Guidelines:
The NA that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer our LLM policy (https://neur http::////auurgeecc///o//rrreres for what should or should not be described.
26


Reviews:
[{"review_id": "UHtRENX3eM", "reviewer": "Reviewer_szBj", "summary": "The paper introduces a method that converts a pre-trained bidirectional video diffusion model into a causal generator of a single function evaluation per latent frame, enabling low-latency, real-time video generation.\nThe key contributions include model architecture and training procedure.\n* In terms of model architecture, the bidirectional attention is replaced by block-causal attention with a sliding window. The clean previous frame is concatenated with noise as input, halving the computation compared to diffusion forcing.\n* In terms of training procedure, it involves first finetuning the adapted architecture with teacher-forcing, then performs consistency distillation, and finally adversarial training. The majority of the innovations are concentrated in the adversarial training stage:\n    * The discriminator for the adversarial training stage is initialized from the same generator weights, adapted to output a per-frame logit.\n    * The student predictions are used as conditioning (student-forcing) instead of ground-truth frames (teacher-forcing), reducing error accumulation and encouraging self-improvement.\n    * To generate long videos beyond the training data, the discriminator is applied in an overlapped, chunk-wise manner. To save memory when training for long video generation, the KV cache of the previous chunk is detached.\n\nThis approach achieves state-of-the-art performance at significantly higher speeds. Additionally, it can be fine-tuned to incorporate conditioning signals for human pose and camera control.", "questions": "* How is student-forcing implemented during adversarial training? It seems that the parallel prediction technique of LLM won’t work for teacher-forcing due to the dependency on previous predictions. Do you pre-sample the student outputs, or chain the sampling process and back-propagate through time?\n* What will happen if the video is generated beyond 60 seconds? How does the failure mode look like?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "### Strength\n* The proposed method excels across multiple dimensions: high-resolution, high-quality, low-latency, and real-time. These are remarkable achievements.\n* The paper is well written, with a comprehensive background introduction and has every important detail documented.\n* Utilizing student-forcing to fundamentally reduce error accumulation is a principled idea that is well-implemented in the proposed model.\n* Compared to the concurrent work self-forcing [1], the proposed method is simpler to implement and potentially faster, thanks to the 1-step generation design.\n[1] Huang et al., Self Forcing: Bridging the Train-Test Gap in Autoregressive Video Diffusion, 2025\n\n### Weaknesses\n* While the proposed method demonstrates strong performance in terms of visual quality and speed, its reliance on sliding window for long-video generation is fundamentally constrained in modeling long-range dependencies. Consequently, it is not clear if the proposed approaches are viable as long-term solutions.\n* There are no visual results provided in the supplementary material.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer for the recognition of our work. We address the reviewer's questions below.\n\n**1. Visual results**\n\nWe have actually provided visual results in supplementary material through an external website. Specifically, Appendix B provided a link to an anonymized website that has all the visual samples and comparisons. We have also provided a five-minute (7200 frame) result in the last section of the page (\"Toward Infinite-Length Streaming\"). This shows our model's behavior when generating beyond 60 seconds.\n\n**2. Student-forcing implementation**\n\nDuring student-forcing adversarial training, the generator generates samples in the exact same way as inference. Specifically, given the first frame (I2V first frame is taken from the dataset), the model recurrently generates the next frame. The frame is recycled as input for the next autoregressive step, along with the KV cache. This recurrent for-loop stops when the target length is reached. This produces the whole video clip.\n\nDuring training, the past frame prediction output is detached before recycling as input for the next autoregressive step. However, the KV state is not detached. So during the backward pass, the gradient can still flow backward through time (through the KV state) to reach all parameters.\n\n**3. The use of a sliding window**\n\nOur work focuses on algorithmic innovation (autoregressive adversarial training, student-forcing, long-video training). We adopted the sliding window technique because it incurs minimal change to the base model. We leave it to future papers to explore long-range memory architectures (for example, inserting a recurrent memory layer, etc.)"}, {"review_id": "EdW4Psegao", "reviewer": "Reviewer_RtHj", "summary": "This paper tackles the challenge of real-time interactive video generation by introducing Autoregressive Adversarial Post-Training (AAPT), a method to convert a pretrained latent diffusion video model into a streaming one-step-per-frame generator. Instead of the usual multi-step diffusion sampling, AAPT produces each new video frame in a single network forward pass (1 NFE) by leveraging a transformer architecture with KV-cache for efficient autoregression. The model is trained with an adversarial objective after initial diffusion training, enabling high-fidelity frame synthesis and student-forced training (training on its own sequential outputs) to mitigate error accumulation over long sequences. Notably, it demonstrates non-stop video streaming up to 1 minute (1440 frames) without quality collapse, significantly outperforming prior one-step or diffusion-forcing methods that were limited to only a few seconds of footage.", "questions": "Training stability and compute cost: Adversarial post-training on an 8B parameter model is a substantial undertaking. Could the authors provide more insight into the training process? For instance, how long did AAPT training take, and were there any instability issues (mode collapse, divergence) that needed to be overcome? Understanding the computational cost and training stability would help gauge the practicality of reproducing or extending this work (e.g., applying AAPT to other base models or at larger scales).", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:** The paper's technical quality is high, offering a novel yet well-founded solution to an important problem. The AAPT approach is **innovative** in combining diffusion models with **adversarial post-training** for video. It builds on recent **diffusion-forcing** ideas but extends them in a new direction. This design fully exploits the transformer's KV cache (like in LLMs) to avoid recomputation, yielding **significantly superior throughput and latency** suitable for real-time use. The empirical results are compelling: the model achieves **state-of-the-art speed** (24 fps at high resolution) far outperforming prior systems like CausVid (which reaches 9.4 fps at lower resolution). It also scales to much longer videos than most diffusion-based methods—*continuous 60+ seconds of video without resets*—whereas competitors like CausVid, SkyReel-V2, and MAGI-1 can only generate in 5-second chunks before needing to restart the generation process. AAPT improves per-frame image fidelity and condition consistency over the diffusion baseline, and achieves the **best overall quality scores** among the compared methods on long video generation. Overall, the authors demonstrate a strong grasp of both the engineering and research aspects, lending credibility to the **originality** of the solution (extending the recent APT idea into an autoregressive domain) and reinforcing the **importance** of the contribution. \n\n**Weaknesses:** The weaknesses of the paper are relatively modest. One concern is that, as with many adversarial generation approaches, **temporal consistency** can suffer slightly; indeed, the authors report a minor drop in temporal quality metrics for AAPT compared to a pure diffusion baseline. There are instances of small **scene or object drift** over very long sequences (e.g., an object that disappears after occlusion, as noted by the authors), indicating that maintaining perfect identity and continuity remains challenging. The proposed solution (AAPT) still relies on a **sliding context window** for simplicity, meaning the generator only looks at a fixed recent history of frames; this could limit long-range coherence and is identified as an area for future improvement. Additionally, the training pipeline is quite complex—it involves diffusion fine-tuning, consistency distillation, and adversarial training with a large 8B parameter model. This raises the **computational cost** and implementation complexity: for example, adversarial post-training on such a large model likely required careful tuning (the paper doesn't detail training stability, which could be non-trivial). Finally, the experiments focus on the image-to-video setting (where the first frame is given); while this is a sensible choice for interactive applications, the method's performance in a pure text-to-video scenario (starting from scratch) is not explicitly demonstrated, a minor omission in scope.", "was_revised": true, "final_justification": "I appreciate the authors' rebuttal and will keep my rating as Accept.", "rebuttal": "We sincerely appreciate the reviewer for the recognition of our work. We address the reviewer's questions below.\n\n**1. Training details**\n\nWe have provided a more detailed training setup in \"Appendix D - Computational Resources\". Specifically, we used 256xH100 GPUs, and the entire AAPT I2V model is trained in approximately 7 days. The diffusion adaptation (stage 1) and long-video training (stage 3) consume the most time.\n\nWe find that our training process is mostly stable after finding the correct hyperparameters (learning rate, batch size, r1 penalty scale, etc., as documented in the paper). Abeit, there are a few times the model diverges and we have to restart from the last proper checkpoints. Another thing to note is that for adversarial training, it is not longer training the better. We have to visualize the checkpoint to see if it reaches the peak quality before quality degrades. The training process still requires more babysitting. We are actively working on further improving the stability of the method.\n\n**2. Pipeline complexity**\n\nAnother good point the reviewer points out is that the current pipeline still requires three stages. We also realize that this causes barriers in production, since every new task must start with diffusion training again. We are actively working on this.\n\n**3. Text-to-video generation**\n\nOur model architecture technically supports text-to-video generation by simply training it to accept an all-zero tensor as the first frame input. We have briefly experimented with it, and the model can indeed perform text-to-video generation. However, we did not choose to pursue this path because T2I one-step generation is still challenging. The APT paper [1] showed that one-step T2I/T2V generation still has a big quality gap. If our autoregressive model performs T2V, the first autoregressive step is equivalent to T2I one-step generation. We are actively working on this more fundamental problem, as improving one-step generation quality can improve our one-step streaming quality as well.\n\n[1] Diffusion Adversarial Post-Training for One-Step Video Generation."}, {"review_id": "lM6HoS1K5H", "reviewer": "Reviewer_oRaR", "summary": "This paper introduces Autoregressive Adversarial Post-Training (AAPT), a method to transform pre-trained latent video diffusion models into efficient autoregressive generators for real-time interactive applications. The core innovation lies in leveraging adversarial training to enable single-step, per-frame generation (1NFE) while mitigating error accumulation through student-forcing training.", "questions": "**Questions about the \"real-time\"**\n\n*1. The lightweight VAE decoder requires a more detailed explanation.*\n\nAblation studies by module are needed to clarify changes in the model's inference speed. Appendix E mentions that the lightweight VAE decoder achieves a 3x speedup compared to the original decoder. Therefore, it is necessary to demonstrate that the model's real-time performance stems from the main innovation(consistency distillation, adversarial training...) rather than other tricks.\n\n*2. Does the rotary position embeddings (RoPE) impact inference speed?​​*\n\nThis work adopts the ​​MMDiT design​​ and employs a ​​sliding window​​ approach for long-video generation, making ​​RoPE particularly important​​ for maintaining positional awareness. The introduction of RoPE brings additional computational overhead. Please clarify how the inclusion of the ​​RoPE module​​ affects the model's inference speed.\n\n*3. More quantitative/qualitative results are needed to demonstrate the effectiveness of post-training.*\n\nThis work has 3 stage (Diffusion adaptation, Consistency distillation, Adversarial training), but it only compares base diffusion models with 3-stage models, it does not clarify the performance changes at each stage. Many models claim to achieve real-time generation (SkyReel-2, CausVid, Matrix, GameNGen), with most employing distillation techniques (consistency distillation, DMD, etc.). However, in practice, distilled models (1-step, 4-step) often underperform the original model (50-step) in terms of human perceptual quality. This paper utilizes post-training to enhance visual fidelity and reduce error accumulation, but it lacks ablation studies to validate the performance of consistency distillation at 1-step, 4-step, and 8-step settings. Additionally, there is a lack of ablation experiments and visual comparisons (unless demonstrated in the project webpage/videos—please point them out if available) to substantiate the visual improvements brought by **Adversarial Post-Training**.\n\n*4. Why autoregressive step is more efficient than onestep diffusion forcing？*\n\nThe paper mentions that the \"recycling approach is more efficient than one-step diffusion forcing,\" yet diffusion forcing can also employ techniques like sliding windows (as in SkyReel-2) and KV caching (e.g., Magi-1, Playable Game Generation, and later works such as VideoMAR and self-forcing). Could the authors clarify the specific differences in how KV caching operates under these two guidance schemes?\n\n**Questions about the \"interactive\"**\n\n\n​​*5. Underperformance in Pose-Conditioned Human Video Generation.​​*\n\nAll metrics of AAPT fall behind OmniHuman-1 in this task. While this is understandable given AAPT's novel motivation, it remains necessary to clarify the ​​unique advantages​​ of this work. Moreover, this work follows the same approach(extract and encode the human pose) as OmniHuman-1, yet its performance is consistently inferior—which is quite strange.\n\n*6. The memory capability of the interactive process model needs clarification.*\n\nExamples on the demo page show that the model exhibits certain memory lapses (e.g., a man in the desert, where background characters suddenly disappear and reappear when the camera angle changes). The original text uses a sliding window approach for interactive generation, lacking a module to record historical information. However, in the field of interactive generation—such as game world models (The Matrix, GameGenX, Genie) and 3D world generation (Wonderland, WonderWorld)—memory plays a particularly crucial role in interactive systems. Please further supplement this in the \"Related Works\" section.\n\n---\n\nOverall, this paper addresses a very interesting and novel problem. However, most of the algorithms are derived from the baseline APT, and the additional autoregressive step proposed in this paper requires further clarification of its advantages compared to diffusion-forcing to address my concerns about the algorithmic novelty. I hope the authors can resolve my concerns.", "limitations": "Yes.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:  \n- This work is written with fluent and clear prose. Rigorous 3-stage training (diffusion adaptation → consistency distillation → adversarial training) enables stable 1NFE generation.  \n- Comprehensive evaluation across 120-frame and 1440-frame generation using VBench-I2V, outperforming diffusion-forcing baselines (CausVid, SkyReel-V2) in throughput/latency .  \n- Ablation studies validate design choices (e.g., student-forcing > teacher-forcing, recycling input necessity).  \n\n**Weaknesses**:  \n- User study is needed to demonstrate effectiveness, although it is not mandatory.If the work already includes human evaluation, please specify details like the number of participants and evaluation methods.\n- Mistake in preparing the manuscript: a paragraph is misplaced (line 224 to line 227), as the author has clarified in the appendix.\n- More concerns about the \"real-time\" and \"interactive\", see the **Questions**.", "was_revised": true, "final_justification": "my concerns have been addressed.", "rebuttal": "We appreciate the reviewer for the thorough review. We address all the questions below.\n\n**1. Lightweight VAE**\n\nFor 736x416 resolution on an H100 GPU, at every autoregressive step, our DiT model takes about 0.120s to generate a latent frame, and our lightweight VAE takes about 0.041s to decode a latent frame (4 video frames).\n\nIn other words, for a 60s 24fps video, the entire video can be generated in 58s (24.8 fps), where the one-step DiT cumulatively takes 43.2s (75%) and the lightweight VAE takes 14.7s (25%). Without the lightweight VAE, the full-size VAE would take 44s (3x), which would make it hard to fit the whole pipeline into a 60s budget. Similarly, without one-step DiT, the pipeline will not fit the budget either. Therefore, both the compressions on DiT and VAE parts are important, but DiT still takes most of the computation. We will add this detail to our final publication.\n\n**2. RoPE's impact on inference speed.**\n\nOur original bidirectional video diffusion model also uses RoPE following MMDiT. The use of RoPE is prevalent in recent video diffusion transformers (Hunyuan, WanX, Seaweed, Seadance, Mochi, etc.). Our work only modifies the frequencies used in RoPE (detailed in Appendix C). This modification is to support arbitrary-length training and inference. Our method's modification to RoPE does not incur additional cost on the inference speed relative to the base model.\n\nThe use of RoPE does incur additional yet marginal per-layer computation compared to absolute positional encoding through input. However, this is similar to the QK norm, which can add inference cost but is commonly used for stable training. Our research focuses on the method of transforming an existing architecture into an autoregressive model. We leave the exploration of more efficient architecture or other possible kernel fusion optimizations to future work.\n\n**3. Effectiveness of adversarial post-training.**\n\nThe reviewer asks for an ablation study between adversarial post-training and consistency distillation to show the effectiveness of adversarial post-training. We would like to clarify that this topic has been more thoroughly addressed in the APT work [1]. Specifically, the APT paper shows that consistency distillation alone generates very blurry results using only a single step, and adversarial post-training improves quality significantly (Figure 8 of the APT paper). The APT paper also shows quantitative VBench metrics comparing adversarial post-training and consistency distillation (Table 7 of the APT paper Appendix B). In our work, we observe a similar phenomenon where consistency distillation alone is insufficient for one-step generation (Our paper, Section 3.2). Since the effectiveness of APT is already established, we focus the ablation studies on the autoregressive generation (long-duration training, recycle input, student-forcing, etc.) which is our main contribution. We refer to the APT paper for more details.\n\n[1] Diffusion Adversarial Post-Training for One-Step Video Generation.\n\n**4. Why is our method more efficient than one-step diffusion forcing?**\n\nFigure 2 of our paper visualizes the difference between our method and one-step diffusion forcing.\nFor example, for 4-step diffusion-forcing, we show the per-frame noise level below. The () indicates frames that must be recomputed on every autoregressive step. The frames outside of () can use KV cache.\n* [(1000)]\n* [(750, 1000)]\n* [(500, 750, 1000)]\n* [(250, 500, 750, 1000)]\n* [(0, 250, 500, 750, 1000)] - compute 5 frames\n* [0, (0, 250, 500, 750, 1000)] - compute 5 frames\n* [0, 0, (0, 250, 500, 750, 1000)] - compute 5 frames\n\nNotice how diffusion forcing needs to compute k+1 frames on every autoregressive step, where k is the number of denoising steps. Therefore, even if we push diffusion forcing to one step, it still must compute 2 frames on every autoregressive step.\n* [(1000)]\n* [(0, 1000)]\n* [0, (0, 1000)] - compute 2 frames\n* [0, 0, (0, 1000)] - compute 2 frames\n\nFundamentally, this is because diffusion forcing recycles past prediction by token concatenation, whereas ours uses channel concatenation. When both using KV cache, our method only needs to compute 1 frame of tokens on every autoregressive step, whereas diffusion forcing needs to compute 2 frames of tokens.\n\n**5. Comparison to OmniHuman**\n\nWe would like to clarify that neither OmniHuman-1 nor other comparison models in the pose-condition and camera-condition tasks are real-time interactive models. Specifically, OmniHuman-1 is a bidirectional video diffusion model that has similar parameters to ours but uses 25 sampling steps + CFG, so it is about 50 times slower than ours. We will improve the clarification in our final publication.\n\n**6. Long-memory architectures**\n\nOur paper mostly focuses on algorithmic contributions and adopts sliding windows for simplicity. We acknowledge the importance of exploring long-range memory architectures, and we leave them for future work (Section 5 discusses limitations). We appreciate the reviewer for pointing out relevant works. We will add them to our final publications.\n\n**7. Originality and novelty**\n\nSimilar to the response to reviewer RpkT, we would like to point out here that, in addition to the autoregressive adversarial training, our paper proposes several other non-trivial contributions worth considering: architectural design (recycle generator that fully exploits KV cache, and parallel causal discriminator that produces per-frame logits), full student-forcing training (gradient pass through recurrent KV state), and long video training (through chunk-based generation and discrimination to address data and memory issues). Specifically, long video training enables our work to be, to the best of our knowledge, the first to achieve minute-long non-stop video generation fully using KV cache; student-forcing shows significant improvement in quality degradation compared to diffusion-forcing methods.\n\nWe hope our response addresses all the concerns. Please let us know if there are any more questions, and we would be happy to address them. We hope the reviewer can reconsider improving the rating to accept our work."}, {"review_id": "Lmj5gxsUOD", "reviewer": "Reviewer_RpkT", "summary": "This paper focuses on enabling real-time, interactive video generation by proposing Autoregressive Adversarial Post-Training (AAPT), a framework that transforms a pre-trained bidirectional latent video diffusion model into a fast, autoregressive generator. The method integrates block-causal transformer architecture with adversarial training, allowing the model to generate an entire latent frame in a single forward pass while using KV cache for efficiency. To mitigate error accumulation, the authors employ student-forcing adversarial training. Experiments show competitive quality and strong controllability in interactive tasks like pose-driven human and camera-controlled scene generation.", "questions": "Please refer to the Weaknesses above.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The proposed method achieves real-time video generation at 24fps and high resolution (736×416 on 1×H100; 1280×720 on 8×H100) using only a single forward pass per latent frame, significantly outperforming existing methods in terms of latency and throughput.\n2. By combining KV cache reuse and a segment-based training strategy, the model is capable of generating one-minute-long videos without stopping, which is notably longer than previous autoregressive diffusion models.\n3. The model demonstrates competitive or state-of-the-art results across three tasks: standard I2V generation (VBench), pose-conditioned human generation, and camera-controlled world exploration, showing both high visual quality and controllability.\n\nWeaknesses\n1. The method extends Diffusion Adversarial Post-Training (APT) [1] to the autoregressive setting, but the novelty is incremental and not clearly disentangled. The key contributions (i.e., causal frame-level generation, student-forcing adversarial training, and efficient use of KV cache) are reasonable adaptations rather than fundamentally new ideas. Besides, the architecture builds heavily upon APT (as the authors themselves note that “Our method extends adversarial post-training (APT) to the autoregressive video generation scenario”). Thus, a more explicit comparison (both conceptual and empirical) to APT would help clarify what is truly novel.\n2. The paper introduces a sliding window attention mechanism to limit memory and compute during long video generation, but it does not explore how the choice of window size $N$ impacts performance. A sensitivity analysis on $N$ would clarify the trade-offs between computational efficiency, temporal consistency, and generation quality.\n3. While the paper reports strong performance on VBench-I2V and other quantitative metrics, it lacks human evaluation of perceptual quality and consistency, especially for the long video and interactive scenarios. Given that many metrics (e.g., FVD, aesthetic scores) are known to be imperfect proxies for human judgment, user studies would strengthen the paper’s claims on quality in real-time applications.\n\n[1] Diffusion adversarial post-training for one-step video generation.", "was_revised": true, "final_justification": "I appreciate the clarifications provided, and I will be keeping my score unchanged.", "rebuttal": "We appreciate the reviewer for the thorough review. We address all the questions below.\n\n**1. Originality and novelty**\n\nIn addition to the autoregressive adversarial training, our paper proposes several other novel contributions worth considering: architectural design (a recycle generator that fully exploits KV cache, and a parallel causal discriminator that produces per-frame logits), full student-forcing training (gradient pass through recurrent KV state), and long video training (through chunk-based generation and discrimination to address data and memory issues). Specifically, long video training enables us to be the first to achieve minute-long non-stop video generation fully using KV cache, and student-forcing shows significant improvement in quality degradation compared to diffusion-forcing methods.\n\n**2. Ablation study on window size**\n\nOur preliminary experiments find that increasing the sliding window size (from 1 to 5 seconds) significantly improves temporal consistency. This makes sense as the model can attend a farther distance. Therefore, for our final model, we choose to use the maximum window size that can fit the real-time and memory budget (5 seconds for 480p and 2.5 seconds for 720p).\nWe have considered adding the ablation study to the window size. However, we realize that we do not have the resources to train multiple models of different window sizes. We have attempted to train one model where the window size is uniformly drawn between 1~5 seconds per sample during training. However, we find that the model trained this way has the best performance at inference when the window size is set to 2.5s, which is the median of the range. Therefore, this experiment is not convincing.\n\nWe would like to emphasize that our paper focuses on the overall algorithmic contributions. The sliding window design is only a simple choice, and we leave exploration for better architecture for future study (discussed in limitations).\n\n**3. Human evaluation**\n\nUnfortunately, due to limited time and resources, we were not able to perform human studies before the deadline of NeurIPS submission. Therefore, we opted for more standardized evaluation metrics, VBench-I2V, because this allows us to compare with previous closed-source models (CausVid, which also reports VBench-I2V) and allows future works to compare with us."}, {"review_id": "6wsCVeMcKo", "reviewer": "Reviewer_XbHv", "summary": "The paper presents a way to turn a pre-trained video diffusion model, trained to generate multiple frames simultaneously to create a fixed length video, to an auto-regressive model that generates the next frame conditioned on the N previous frames.\n\nThe method consists in 3 steps:\n* First the model is adapted to take as input not just the noise, but the concatenation of the previous frame to the noise (channel wise). The model is also adapted to use causal attention (frame by frame), turning it to an auto-regressive model.\n* Then the model is distilled to generate the next frame in one step\n* Then an adversarial loss is used to turn that frame into a higher quality frame, and student-forcing is used so that inference and training do not suffer from a mismatch in distribution that would make inference diverge quickly\n\nThanks to those adaptions, the authors claim that:\n1) the model can generate 24fps real time, with a resolution of 736×416 on a single H100 GPU and 1280×720 on eight H100 GPUs\n2) the model can generate minute long videos thanks to its auto regressive nature", "questions": "n/a", "limitations": "n/a", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Although the idea of one step distillation using GAN loss is not new, this paper introduces some nice tricks that make it better and combine it with auto-regressive generation frame by frame, to make video generation real time.\n\nStrengths are:\n- Great results in terms of generation speed\n- An interesting combination of tricks to speed things up and push quality further (like frame by frame GAN loss, or student forcing)\n- Convincing results, particularly for long 1 minute videos\n\nWeaknesses:\n- The evaluation protocol is relying on VBench-I2V which might not be the best correlated with human judgement on recent models, on qualitative evaluations, but no human evaluations (the golden standard)\n- The benefits of the methods are most seen on long videos, but the videos shown are also very repetitive (there can be no scene change or interesting motion that change the layout too much with this approach)", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate the reviewer for the recognition of our work. The reviewer did not post any questions, but we acknowledge and agree with the weaknesses that the reviewer has pointed out. These are indeed issues that we aim to resolve and do better in future works. We thank the reviewer for the positive assessment of our work."}]

Author rebuttal:

Meta-review: The paper explores a method to leverage pre-trained video diffusion models for real-time video generation. The reviewers found the method to be novel, sound, and an improvement on existing approaches.
Decision: Accept (poster)