TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation
Authors: Hongyu Zhang
Yufan Deng
Zilin Pan
Peng-Tao Jiang
Bo Li
Qibin Hou
Zhen Dong
Zhiyang Dou
Daquan Zhou
Venue: ICLR 2026 Poster (2026)
Keywords: Video generation
Diffusion model
Primary area: generative models

Abstract: Generating high-quality videos from complex temporal descriptions, which refer to prompts containing multiple sequential actions, remains a significant challenge. Existing methods are constrained by an inherent trade-off: using multiple short prompts fed sequentially into the model improves action fidelity but compromises temporal consistency, while a single complex prompt preserves consistency at the cost of prompt following capability. We attribute this problem to two primary causes: temporal misalignment between video content and the prompt, and conflicting attention coupling between motion-related visual objects and their associated text conditions. To address these challenges, we propose a novel, training-free attention mechanism, Temporal-wise Separable Attention (TS-Attn), which dynamically rearranges attention distribution to ensure temporal awareness and global coherence in multi-event scenarios. TS-Attn can be seamlessly integrated into various pre-trained text-to-video models, boosting StoryEval-Bench scores by 33.5% and 16.4% on Wan2.1-T2V-14B and Wan2.2-T2V-A14B with only a 2% increase in inference time. It also supports plug-and-play usage across models for multi-event image-to-video generation. The source code and video demos are available in the supplementary materials.

Published as a conference paper at ICLR 2026
TS-ATTN: TEMPORAL-WIL WISE SEPARABLE ATTENTION FOR MULTI-EVENT VIDEO GENERATION
Hongyu Zhang': Yufan Deng'; Zilin Pan
Peng-Tao Jiang",
Bo Li,
Qibin Hou?, Zhen Dong' Zhiyang Dou
Daquan Zhou If
!Peking University, Shenzhen Graduate School
vivo Bluelmage Lab Nankai University University of California, Santa Barbara Thee University of Hong Kong { (zhanghyestu, dengyufanl
daquan zhouê pku. edu. en
dragon emerges from cave, spreads its wings, breathes fire into the sky and then flies off cliff.
beach from morning high tide, sunny midday waves, to evening tide, and finally moonlit waters under the stars.
humanold robot glass, places bottle table, and then lifts the glass,
Human
(a) Qualitative results
Animal
Average Score
-16.4% Score Latency
Skyftedl- V2-148
Hard
Object
MEVS - Wan2.2-4148
0.4
Easy
Retrieval
1000
1500
2000
2500
Creative
Latency (s)
(b) Quantitative comparison
(c) Inference latency comparison
Figure 1: We present TS-Attn, training-free attention mechanism, which enhances multi-event video generation through alleviating attention conflicts across multi-event conditions. (a) Qualitative results across subjects and scenes. (b) Quantitative comparison on StoryEval-Benche (c) Latency- performance tradeoff analysis.
ABSTRACT
Generating high-quality videos from complex temporal descriptions that contain multiple sequential actions is key unsolved problem. Existing methods are constrained by an inherent trade-off: using multiple short prompts fed sequen- tially into the model improves action fidelity but compromises temporal consis- tency, while a single complex prompt preserves consistency at the cost of prompt- following capability. We attribute this problem two primary causes: 1) temporal misalignment between video content and the prompt, and 2) conflicting attention coupling between motion-related visual objects and their associated text condi- tions. To address these challenges, we propose novel, training- ffree attention mechanism, Temporal-wise Separable Attention (TS-Attn), which dynamically
Equual contribution.
Corresponding author.

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Published as a conference paper at ICLR 2026
rearranges attention distribution to ensure temporal awareness and global coher- ence in multi-event scenarios. TS-Attm can be seamlessly integrated into various pre-trained text-to-video models, boosting StoryEval-Bene BellBBench scores by 33.5% and 16.4% on Wan2. 1-T2V-14B and Wan2.2-12V-AI1 144 with only a 2% increase in inference time. It also supports plug-and-play usage across models for multi- event image-to-video generation. The source code and project page are available at https///giiub..com/Hogg Suainng-y--hhaT-AAAAiii
INTRODUCTION
Video generation models have undergone remarkable advancements, demonstrating impressive progress in their generation capabilities Blattmann et al. (2023a); Wang et al. (2024); Kong et al. (2024 Deng et al. (2026), which has in turn sparked wide range of downstream applications Deng et al. (025bb); Hu et al. )025); Deng et al. (2025a); Guo et al. (224).. Through the optimization of model architectures Peebles& Xiel (2023): Flux (2024) and the scaling of training data Wang et al. 2025a), current models are capable of generating high-quality videos. However, the current good performance is mostly limited to the prompts containing single events, even for the state-of-the-ee art open-source models Wang et al. (2025a); Yang al. (2024); Kong et al. (2024). How to faithfully generate videos from complex temporal descriptions (c.g., containing multiple events and dynamic motion information) remains underexplored.
Existing approaches can be broadly categorized into two streams, each facing inherent performance trade-offs. The first stream decomposes complex multi-event prompt into several single-event prompts and executes them across multiple inference stages Lin et al. (2023); ZZhng et al. (0024)
While this paradigm is capable of producing action-rich content, combining individually generated clips using techniques such as KV cache Cai et al. (2025) or initial noise optimization Oh ct al. (2024) often results in content drift and pronounced temporal inconsistencies Kim et al. (2025)
with significantly increased inference time overhead. Conversely, the second stream of methods directly feeds the entire complex multi-event prompt into more powerful text encoders. Although this paradigm yields videos with improved consistency and global coherence WWang et al.] (2025a); Zhang& & Agrawala (2025), it often exhibits limited prompt-following ability, failing to accurately interpret and respond to all individual events. Such limitations frequently manifest as event omission or temporal hallucination.
Achieving optimal trade-off requires simultancously balancing global consistency and prompt adherence. This raises a key question: can we preserve global coherence with a single complex prompt while ensuring that the video accurately responds to each event in the correct temporal or- der? As illustrated in Figure 3 our analysis shows that the primary cause of weak prompt-following lies in temporal misalignment and the entangled attention correlations between motion-related re- gions of video tokens and the textual conditions of multiple events. To address this, we propose simple yet effective idea: disentangle the video-text attention distribution associated with differ- ent events in the prompt and realign it with the corresponding individual events, ensuring that they remain separable along the temporal dimension with proper transitions.
From this observation, we derive two key insights: (I) motion-related regions in each frame should focus primarily on the event that occurs at the same time, and (2) interactions across different events in the temporal dimension should be minimized.
Building on the above insights, we propose Temporal-wise Separable Attention (TS-Attn), a method that dynamically adjusts the attention distribution in the cross-attention layer to enable tem- poral awareness in multi-event scenarios. Our idea intuitive: TS-Attn first extracts and thresholds the cross-attention map associated with the event- pent-peeforming entity to identify the motion-related regions. TS- Attt then rearranges the attention distribution between motion-relate video tokens and each event condition with proper separation to strengthen the correspondence with the temporally aligned event while reducing attention coupling from unrelated events. Finally, TS-Attn incorporates an attention reinforcement mechanism that adaptively scales event-related attention values based on the attention distribution: a smoother distribution indicates that more modifications are needed.
In summary, the key contributions of our work are as follows:
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Published as a conference paper at ICLR 2026
Prompt:
cat watches bowl filled with water, dips paw inside, and then takes it out,
+TS-Attn
Wan2.2-A148
"watches"
ddips
"tokes out
Attention map along the temporal sequence
Figure 2: Comparison attention maps along the temporal sequence between TS-Attn and vanilla cross-attention. TS-Attn strengthens motion-event alignment and reduces cross-event in- terference, ensuring accurate attention distribution among multiple events.
We conduct an in-depth analysis of the root causes underlying poor prompt-following performance in complex descriptions, and reveal that temporally separable grouping essential prevent temporal conflicts.
We propose novel framework, TS-Attn, which dynamically restructures the attention distribution between motion-related regions and multi-event conditions. This design enables accurate event responses in the correct temporal order, while simultaneously preserving global consistency and ensuring physically plausible transitions.
We conduct extensive experiments demonstrating that TS-Attn can be used in training-fres man- ner and seamlessly integrated into diverse video generation foundation models. Both qualitative and quantitative results show that substantially improves baseline performance with negligible inference overhead, while remaining effective across multiple tasks, including multi-event text-to- video (T2V) and image-to-video (I2V).
RELATED WORKS
Diffusion-based video generation. Initial efforts concentrated on integrating temporal attention mechanisms into the 2D U-Net architecture |Ronneberger et al. (2015), allowing image genera- tion models to better capture the temporal dynamics required for video synthesis Blattmann et al. (023b): Wang et al. (2023b); Khachatryan et al. (2023); Chen et al. (024).. As diffusion trans- formers (DiTs) gained prominence Ma al. (2024), the focus shifted towards employing 3D full attention, effectively bridging spatial and temporal dependencies [Zheng et al. (2024); Lin et al. 2024); Zhang et al.] (2025a). This innovation laid the foundation for scalable models such as Cog VideoX Yanng et al. (2024). LTX-Video HaCohen et al. (2024), Hunyuan Video Kong et al. (024), and Wan Wang et al. (2025a), which advanced the generation of high-resolution, tempo- rally consistent video content.
Multi-event video generation. Several studies address multi-event video generation by breaking it into sequential multi-prompt generation Wang et al. (2023a); Qiu et al. (2024); Kim et al. (0055). MEVG Oh et al. (2024) ensures visual coherence by initializing each clip's noise with the inverted last frame of the previous clip, while DiTCtrl et al. 2025) enables smooth motion transitions via mask-guided key-value sharing. However, these approaches require repeated inference, increasing computational costs and causing temporal inconsistencies. Another line of methods uses local and
global cross-attention to strengthen responses to multiple sub-prompts Wang et al. (2025c): Tian
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Published as a conference paper at ICLR 2026
Initial Noise
DIT Block
X
TS-Attn
Self Attn
Cross-Attn
3D VAE
Prompt
T Steps
Decode
wotther bowl.
diles pow inside, and)
then tokee
1x0.27
Query
Baseline
+TS-Attn
MIQ.K]
Key
Erosion Func
R1Q.A)
"dips"
R1Q.8)
B(Q.K) krantis inutappreee
R(Q K) Anartor
Motion Region Extraction
Event-aware Attention Modulation
"take
Figure 3: The overall framework TS-Attn TS-Attn replaces the original cross-attention in early denoising stages incorporate motion information with temporal awareness. It consists of motion region extraction module to identify motion-relater tokens and an event-aware attention modulation module to adjust their attention distribution across multiple events.
et al. (0024; Bansal et al. (024). However, the use of hard-masked attention Tian et al. (2024); Bansal et al. (2024) for overly strict control can lead to issues such background inconsistency and makes it difficult to process fine-grained temporal transitions when foreground subjects are small.
To address this issue, recent approaches focus packaging individual events into global prompt for single-pass inference generation. Among them, Min'' Wu et al.) (2025b) and ShotAdapter Kara et al. (2025) rely large amounts of timestampplabel labeled data for post-training enable the model to handle multi-event scenarios. However, this requires extensive computational resources and is diffi- cult adapt to new models. An intuitive approach
use more powerful video generation founda- tion models, with features such as the ability to handle more complex prompts (e.g., Wan Wang et al. (2025a). Hunyuan Video Kong al.) (2024)) and longer frame durations (e.g.. Framepack Zhang & Agrawala (2025). SkyReels-V2 V2 Chen et al. (2025). MAGI-1 [Teng et al. (2025)). Yet in practice, these models still struggle with complex multi-event prompts, often leading to event omissions or temporal coupling, underscoring the need for more robust solutions.
3 METHOD
3.1 INSIGHTS OF TS-ATTN
We conduct an in-depth analysis of why existing state-of-the-art foundation models encounter is- sues such as event omission and temporal errors when single sentence contains multiple events. Specifically, we examine whether the keyframes of the generated video establish the correct tem- poral correspondence between video tokens and event conditions within the cross-attention layer. Since motion information is primarily formed in the early stages of denoising Zhang et al. (2025b), the middle layer at 20% of the denoising steps is used for attention analysis.
As illustrated on the right of Figure 2 we identify two critical issues in the cross-attention distri- bution of Wan2.2-Al1 14B: (I) Motion-related regions (i.e., the layout the subject "cat") in each frame fail to establish strong attention associations with their corresponding verbs in the temporal sequence. For instance, "watch" loosely aligns with the layout of "cat". while actions like "dips" and "take it out" focus on irrelevant background areas, leading to severe misalignment. (2) Attention coupling of verbs from different events occurs within the same frame. For example, in the middle frame, all three verbs exhibit strong responses the same video regions.

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The issues discussed above lead to incorrect injection of multiple event conditions, resulting in severe event omission and temporal errors. This phenomenon indicates that the cross-attention map requires significant recalibration to accommodate the temporal distribution of multiple events.
To address these issues, TS-Attn is designed based on two core insights: 1) Strengthen the atten- tion correlation between each frame's motion-relevant region and its corresponding temporal event; 2) Minimize interference caused by coupled attention across different events. As expected, the implemented TS-Attn significantly improves temporal attention alignment across multiple events, ensuring faithful generation of multi-event sequences (Figure left).
3.2 OVERALL FRAMEWORK
As shown in Figure the overall framework is implemented based on the DiT architecture. We replace the original cross-attention with TS-Attn in the early denoising stages to inject motion in- formation with stronger temporal awareness. TS-Attn consists of two components: first, it identifies motion-region video tokens using the motion-related subject semantic layout, then applies event- aware attention modulation to these video tokens.
The temporal segmentation of multiple event intervals for video tokens can be simply achieved through various methods, including user input, leveraging efficient LLM APIs (c.g.. GPT-4o-mini), or default uniform segmentation. These approaches show minimal differences in final performance. Details can refer to Appendix Table By default, we use GPT-4o-mini for temporal segmentation, unless otherwise specified in the context.
3.3 MOTION REGION EXTRACTION
To achieve precise attention modulation, TS-Attn first adaptively identifies motion regions across the video. Motion information in video primarily originates from the foreground subject performing actions. Thus, the semantic layout of the subject in the prompt can approximately represent motion- related regions. As shown in Figure given the query of the video tokens Q IR. Nxx and the key of the text tokens K E I xd we obtain the semantic map A, E ERN RRXI of the subject s:
(I=(O <QK
A, Mean
Vd
where (-) represents the function for indexing subject s. Similar to Helbling et al. (2025). ) we use the mean value of A, adaptive threshold to obtain the motion region mask M, E R x1:
M, Fx (I,A Mean(A.)))
where Fx(.) represents the erosion function with a kernel K. which used to remove seattered noise and refine the boundaries of the binary mask. Experimentally, AC is set to 3. Finally, we can use M, to guide attention modulation in motion-related regions.
3.4 EVENT-AWARE ATTENTION MODULATION
To address the temporal misalignment and coupling of multi-events observed in Figure event- aware attention modulation TS-Attn is divided into two components: attention rearrangement and attention reinforcement.
Attention rearrangement is directly based on the insight from Sec. 3.1 It redistributes the attention in cross-attention along the temporal dimension, ensuring that motion-related video tokens in each frame focus on their temporally corresponding events while attenuating attention to other events. Attention reinforeement adaptively adjusts the intensity of attention based on the sharpness of the attention distribution, ensuring balanced and event-aware attention scaling. Therefore, the entire attention modulation process TS-Attn can be formulated follows:
QK + M, O R(Q, K) o B(Q,K) E NxM
A = softmax
(3)
Vd
where B(Q,K) K)) is the bias function to achieve attention rearrangement, R(Q, K) is the attention reinforcement function, and M, is derived from Sec. 3.3 to constrain attention modulation in the motion-related region. The details of these two functions introduced below separately.
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Attention Rearrangement. Given the event token list (e1, .g...., Cml in the prompt, and the corresponding temporally segmented video queries [Q1, Q2..... Qm] as described in Sec. 3.2 Attention rearrangement encourages each temporally segmented video query to interact with its corresponding event while weakening the influence of other events. This process mainly achieved by applying positive hias bú negative bias b; to different events:
b max (Q:K)
mean mean(Q, (Q:KT,,
(4)
b; = min min(Q,KK) mean (Q,K²)
(5)
b 1e ul,
if ei, y E e otherwise,
B (QQ, (3 vl
b [| y),
0,
where (QQ K) is the bias function for Q, and [z, yl represents the indices of the query and key. For Q positive bias applied eq, while a negative bias applied other events. The remaining text is treated as prompt context, with no bias applied.
Finally, we obtain the bias for each segmented video query in similar manner and concatenate them together to obtain the complete bias function (QQ,, K):
[ K) B(Q K) + B(Q2. K).. @ B(Qm. K) E
where @ indicates the concatenation function.
Attention Reinforcement. We observe that when the attention between Qi and e; is not salient enough and the overall distribution is overly flat, it is still difficult to achieve temporal alignment solely through attention rearrangement. To address this, we further leverage attention reinforcement to adaptively strengthen the focus on the temporally aligned event by additionally introducing reinforcement factor term R(Q, K) to attention rearrangement.
Specifically, we first obtain the original distribution of attention probes p; Softmax
Q.K" and
Va
measure the attention intensity of each text token after normalization as p P.-p. pr"+t" Subse-
quently, can adaptively adjust the positive strengthening factor r and the negative strengthening factor rū based on p.. Specifically, when pi is small for a temporally aligned event e, or large for other events, the intensity needs be increased accordingly:
min + (1 P¹) (n min)
(8)
ri min + p/: (r ,in)).
(9)
where min and max are the lower and upper bounds of the strengthening factor, which experi- mentally set to 1 and 1.5, respectively. Then R(Q,, K)) can be formulated as:
if E ei, M] E eg,i 0,
r [1, vl,
R(Q,, K) [))
(10)
otherwise,
Finally, we obtain the complete RRQ, K.) to match ((Q,K).
R(Q,K) R(Q K) @ R(Q,, K).. e R(Q K) NXM
(11)
For simplicity, we illustrate the process of TS-Attn using the prompt containing only a single subject. The details for handling prompts with multiple subjects can be found in Appendix A
4 EXPERIMENTS
4.1 EXPERIMENTAL. SETUP
Implementation Details. We seamlessly integrate TS-Attn into multiple video generation models, including: (1) VodVid Yang et al. (2024) based the MM-DiT architecture, and (2) Wan2.1

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Published as a conference paper at ICLR 2026
and Wan2.2 models Wang al. (2025a) based the Cross-DiT architecture, which injects text conditions through cross-attention. We perform both T2V and I2V tasks on these models. For the T2V task, TS-Attn is applied to the first 20% of the denoising steps. For the I2V task, the first 40% of the denoising steps are selected to enhance control effects. Basic inference settings such as the number of denoising steps, the scheduler type, and video resolution remain consistent with the original configurations of these models, All experiments are conducted on NVIDIA A100 GPU.
[0,1,1,0]
MMG333
[1,0,0,0]
[0 [0,1,1,0]
10,0,0.01
[11,111]
15-4000
ball rolls towards wheelchair, collides the wheelchair and bounces back, and then rolls slowly.
Figure 4: Qualitative comparison results on multi-event T2V generation. The list in the top-left corner, evaluated jointly by GPT-4o and humans, indicates the completion status of events.
M60- 24k
[1,,,0]
Wunze -118 Septett-2--1 14
[0,0,0]
[1,0,0]
The robotic arm grosps the carefully. pours water into the potted plant, and then places the cup back on the counter.
Figure 5: Qualitative comparison results on multi-event I2V generation. The list in the top-left corner, evaluated jointly by GPT-4o and humans, indicates the completion rates. SkyReels- V-- 14B generates actions that defy the laws of physics, resulting in a completion score of zero for all events.
Baseline Models. The comparison models we selected can divided into three categories: (1)
Basic video generation models, which include Open Open-Sora-Plan 1.3.0 et al. (2024), Open-Sora 1.2 Zheng et al. (2024), Vchitect-2.00 Fan et al. (2025). Pyramid-Flow Jin et al. (2024), SkyReels-
V2 Chen et al. (2025), and MAGI-I Teng et al. (2025 (2) Multi-event video generation models, which includes MEVG Oh et al. (024) and DiTCll Cai et al. (2025) reimplemented on Wan2.2-
A14B; (3)
models, including KlingAl| (2024), and HailuoA| (2024). Training-
based methods MinT Wu et al, (2025b) and ShotAdapter Kara et al. (2025) are excluded due to closed-source code and data.

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Published as a conference paper at ICLR 2026
Benchmark and Evaluation Metrics. We select Ceoryvaal-Bende Wang al. (2025b) for the quantitative evaluation of multi-event T2V tasks, as is a representative benchmark containing 423 prompts across seven classes, with 2-4 events per prompt. This benchmark utilizes GPT-4o OpenAl (2024) and LLaVA-OV-Chat-225
al. (2024) to evaluate event completeness, temporal accuracy, and subject consisteney in the generated videos. Since existing multi-event 12V benchmark is available, we construct StoryEval-Bench-12N Specifically, GPT-4o is used to reparse each prompt describe the initial state of the video, and Qwen-Image Wu et al. (2025a) synthesizes the first frame according to the reparsed prompt. Further details can be found in the Appendix B
Table I: Evaluation results on T2V tasks with GPT-4o verifier. Best scores are bolded.
Model
Human
Animal
Object
Retrieval
Creative
Easy
Hard
Average
Hailuo
38.2%
38.3%
27.5%
42.6%
18.0%
58.9%
9.7%
35.1%
Kling-1..5
37.2%
44.9%
36.6%
39.4%
36.0%
60.8%
16.4%
40.1
Open-Sora-Plan 1.3.0
9.1%
9.7%
9.4%
13.2%
7.1%
18.2%
3.2%
9.4%
Open-Sora 1.2
16.4%
18.3%
16.2%
24.7%
11.8%
32.7%
4.3%
17.9%
Vchitect-2.0
21.5%
19.9%
20.4%
22.0%
15.2%
42.8%
3.9%
21.7%
Pyramid-Flow
17.8%
16.5%
12.8%
23.4%
9.7%
35.1%
1.0%
16.0%
SkyReels- V2
43.8%
39.9%
35.4%
43.1%
27.0%
55.9%
26.7%
40.6%
MAGI-1-244
39.6%
32.7%
33.5%
41.9%
24.8%
51.7%
20.5%
35.8%
MEVG Wan2.2-A14B
47.7%
39.7%
40.5%
47.6%
28.3%
57.8%
28.9%
43.1%
DiTCCr Wan2.2-A14B
50.5%
48.4%
39.8%
57.9%
26.2%
60.1%
33.4%
46.5%
CogVidoo
17.1%
16.4%
14.0%
16.0%
7.4%
35.4%
4.6%
16.4%
+Ours
28.0%
25.4%
21.7%
32.9%
13.9%
45.7%
9.9%
25.8%
1-n2.1-1.3B
32.4%
31.0%
24.9%
30.6%
22.1%
42.3%
17.6%
29.1%
+Ours
43.1%
33.9%
34.6%
47.0%
24.5%
53.2%
23.5%
37.6%
Wan2.1-14B
41.4%
37.2%
31.9%
45.2%
21.9%
53.8%
24.6%
37.6%
+Ours
54.7%
50.0%
45.1%
62.1%
35.2%
64.5%
38.7%
50,2%
W-12.2-A14B
51.2%
46.7%
44.9% 52.0%
54.8%
34.8%
60.3%
34.0%
48.3%
+Ours
60.4%
53.6%
63.0%
45.3%
70.5%
44.3%
56.2%
Table 2: Quantitative comparison results on 12V evaluation tasks with GPT-4o verifier.
Model
Human
Animal
Object
Retrieval
Creative
Easy
Hard
Average
Framcpack-13B
37.3%
30.9%
28.2%
45.0%
21.1%
43.9%
25.3%
33.5%
SkyRecls-V2-I2V-1
40.5%
37.9%
34.1%
41.1%
25.5%
43.7%
28.0%
36.9%
MAGG-1-I2V-24B
37.2%
31.3%
32.6%
37.0%
19.4%
44.7%
26.7%
34.2%
Cog VogVexXX-22--B
21.0%
18.8%
17.5%
23.3%
10.0%
35,8%
9.9%
19.6%
+Ours
28.2%
28.8%
23.5%
35.
16.5%
44.3%
15.9%
28.3%
Wan2. 1-12V-141
43.8%
33.9%
36.0%
42.1%
29.8%
44.4%
31.9%
37.0%
+Ours
46.0%
38.8%
43.3%
44.9%
32.0%
54.2%
32.6%
42.6%
48.4%
49,3%
43.1 1%
50.3%
34.4%
57.8%
39.1%
47.5%
+Ours
58.3%
53,2%
50.4% %
63.0%
36.5%
64.0% %
43.8% %
54.4%
4.2
QUALITATIVE COMPARISON
Figure Ia) presents representative examples generated by our method, showcasing its robust capa- bility to handle multi-event generation tasks. In particular, Figure 4 illustrates results for multi-event T2V generation, where the ball interacts with wheelchair, demonstrating smooth sequence of events, including rolling, collision, and subsequent movement. Additionally, Figure [ highlights multi-event 12V generation, showing robotic arm performing tasks such grasping, pouring, and placing an object. In both cases, our method effectively captures the interactions and transitions between events, with GPT-4o and human evaluations jointly assessing the completion status. This comparison underscores the model's ability to handle complex, multi-step sequences across various scenarios, emphasizing its effectiveness, and robust generalization in diverse video generation tasks.
4.3 QUANTITATIVE COMPARISON
Benchmark Comparison. As shown in Table [ incorporating TS-Attn into Wan2.2-A14B. Wan2.1-14B, 1an2.1-1.3B, and VogVideoX-SB significantly improves baseline performance across different architectures and scales. For example, observe relative improvements of 33.5% and
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57.3% on the StoryEval-Bencee Beench score in Wan2.1-T2V- and CogVideoX-SB models, respectively. This clearly demonstrates the versatility of TS-Attn across various model architectures. Besides, when using Wan2.2-A14B as the baseline, TS-Attn significantly outperforms DiTCtrl and MEVG, which are based on the multi-prompt paradigm. This further demonstrates TS-Attn's 's excellent trade- off between temporal consistency and prompt-following.
Table 3: Inference time comparison on single A100 GPU for different models.
Model vky MAGI-I-24B 1865
Wan2.2-A14B
+MEVG
+DiTCul +IS-Attn(Ours) 2749
Latency (8)
2732
846
2453
863
Table 4: Ablation results of TS-Attn on StoryEval-Bench.
Method
Wan2.2-A144B
Wan2.1-14B
Vid
Easy
Hard
Avg
Easy
Hard
Avg
Easy
Hard
Avg
Baseline EAM
60.3%
34.0%
48.3%
53.8%
24.6%
37.6%
35.4%
4.6%
16.4%
66.2%
39.8%
51.9%
62.6%
31.1%
46.4%
42.1%
7.3%
22.9%
EAM MRE
70.5%
44.3%
56.2%
64.5%
38.7%
50.2%
45.7%
9.9%
25.8%
In the 12V task, TS-Attn consistently brings performance improvements across various baseline models, as shown in Table [ Overall, the extensive experiments above demonstrate the excellent performance of TS-Attn across various tasks and model architectures. Further quantitative compar- isons evaluated using
are provided in the Appendix Table 5 Table
Inference Efficiency Analysis. We compare TS-Attn with other models in generating 480x832 videos to evaluate inference efficiency. For single-prompt models, the frame count is fixed at 81, while for multi-prompt models (e.g., DiTCtrl, MEVG), it approximately 81 x n, where n denotes the number of events in the prompt. The average response time for temporal segmentation using GPT-4o-mini is 2.65 seconds, which is also included in the overall inference time. The average inference time on StoryEval-Bencee is used for comparison. As shown in Table 3 TS-Attn increases inference time by only 2% compared to Wan2.2-A14B, while significantly outperforming models like DiTCtrl and MAGI-1-24B.
4.4 ABLATION STUDY
Event-aware Attention Modulation. We verify the effectiveness of event-aware attention modu- lation (EAM). As shown in Table EAM significantly improves baseline performance by 23.4% on 1-n2.1-14B and 39.6% on Cog CogVideoX-SBB validating its effectiveness. We also conduct an in-depth analysis of the attention rearrangement and attention reinforcement subcomponents within EAM. As attention rearrangement contributes more to performance improve- ment, validating its effectiveness in temporally aligning multiple events. Attention reinforcement, on the other hand, serves more as a supporting component, adaptively adjusting the strength of attention rearrangement to accommodate diverse cases.
illustrated in Appendix Table 7
Motion Region Extraction. We also analyze the role of the Motion Region Extraction (MRE) module. As shown in Figure 77 MRE constrains attention modulation to motion-related regions, ensuring the precision of modulation while avoiding interference with the cross-attention distribu- tion of background video tokens, thus preventing issues such abrupt scene changes. Table quantitatively validates the effectiveness of MRE.
Different Temporal Segmentation Methods. Finally, we discuss the impact of different temporal segmentation methods on performance. As shown in Appendix Table the performance differences among uniform segmentation, human annotation, and GPT-4o-mini planning minimal. This indicates that TS-Attn only requires a rough temporal segmentation to effectively perform reasonable attention reallocation. More discussions can be found in the Appendix
5 CONCLUSION
In this work, we introduce Temporal-wise Separable Attention (TS-Attn), a novel attention mecha- nism designed to address the challenges of generating videos from complex temporal descriptions.

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The mechanism dynamically reallocates attention ensure both temporal consistency and global coherence, effectively overcoming the trade-offs between action fidelity and prompt adherence ob- served in existing methods. Experimental results demonstrate that TS-Attn enhances the perfor- mance of pre-trained text-to-video models, yielding significant improvements in StoryFval-Bende scores with minimal impact on inference time. Moreover, TS-Attn operates as plug-and-play lution, making it compatible with variety of models for multi-event image-to-video tasks. This approach represents significant advancement in scalable, high-quality video generation capable of handling complex and temporally dynamic input prompts.
ACKNOWLEDGMENTS
We thank all the anonymous reviewers for their constructive comments. This work was supported in part by the Peng Cheng Laboratory 00.2044KF1A000001
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TS-Attn: Temporal-wise Separable Attention for Multi-Event Video Generation
Appendix
A TS-ATTN FOR MULTIPLE SUBJECTS
For brevity of description, we introduce TS-Attn in the main text using single subject and its cor- responding event list. Therefore, this section provides a supplementary explanation for scenarios involving multiple subjects. Given prompt with subject list ( 82......... 8m], we iteratively ex- tract the motion-related region mask for each subject, resulting in [Ma May...., M... Similarly, based the temporal distribution of the event list corresponding to each subject, we derive the attention rearrangement terms [B,, (Q, Bag (Q, K). K).... ...,,,,. (Q, K)] and attention rein- forcement terms [R,, (Q, K) R.g (). R.,.. (Q, K)) for every subject. We can then obtain the final modulated attention map by summing the bias terms of all subjects:
E A = softmax QK (QKT
Ms, O Rs, QQ K)
Bal (Q,
E RRM
(12)
vd
is worth noting that for multiple subjects, our implementation avoids repeated computation of the attention matrix. Instead, only sequentially index the attention values at required positions for each subject to construct the bias terms. As a result, the inference overhead for multiple subjects remains nearly identical that single subject.
B CONSTRUCTION PIPELINE FOR STORYEVAL-BENCH-IVV
StoryEval Bench
Prompt Reparse
Source Prompt: "A bartender shakes cocktail, pours It into a glass, and then garnishes it with a lemon wedge."
423 prompts
Target Prompt
Edit Instruction: "Change the video prompt describe the initial state before all events happen."
classes
Target Prompt: "A bartender stands behind a bar with a cocktail shalter, a glass, and a lemon wedge prepared, ready make drink."
T2V
StoryEval Bench 12V
Qwen-Image
Qualified!
Recycle times
prompts
7 classes
12V
Figure 6: Construction pipeline of StoryEval-Bench-D2
Due to the absence of dedicated multi-event I2V benchmark, construct evaluation frame- work to assess the generalization ability of TS-Attn on I2V tasks. StoryEval-Benae Wang et al. (025b), representative benchmark for multi-event text-to-video generation, has undergone peer review and features a large scale of prompts with high data diversity. Based on this foundation, we explore extending StoryEval-Bene -eeh-B support the I2V task.
The core lies in deriving a reasonable first frame image from the video prompts in StoryEval-Bendee As illustrated in Figure 6 first use GPT-4o OpenAl (2024) convert source video prompts into
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target descriptions of the initial state before any events occur. These descriptions primarily include static information such as the subjects and background layout involved in the video prompt, and approximate representation of the first frame of the video. We then can therefore be regarded text-to-image model Owen-Image Wu et al." (2025a) to synthesize the employ the state-of-thee first frame of the video based the target descriptions. To ensure the aceuracy of synthesized images, we select three different random seeds for synthesis and manually identify the optimal im- age. Through this process, obtain 423 image-text pairs support I2V task validation. Since do not alter the prompt categories in Berryvvl-Bennde we use the original benchmark's 's evaluation methodology for assessing the generated videos.
[1,0.0]
[1,0,0]
w/ MMR
[1,1,1]
T5 atto HMu
bicycle pedals itself down the street, stops ot a red light, and then continues when turns green,
Figure 7: Ablation results on the effect of motion region mask. Not restricting attention modu- lation to motion-related regions can, in some cases, lead to background flickering, which ultimately degrades the overall video quality. Additionally, hinders the motion regions from effectively re- sponding to individual events.
C MORE COMPARISON RESULTS WITH LLAVA-OV-CHAT-72B VERIFIER
As shown in Tables 5 and 6 we also employ the LLaVA-Ov-Chat-22 Li et al. (024) verifier to evaluate the generated videos. Consistent with the conclusions drawn using the GPT-4o verifier, TS-Attn consistently and significantly improves baseline performance across multiple models and both I2V and T2V tasks.
D ABLATION RESULTS OF EAM
The core of AStAttn event-aware attention modulation, primarily consists of two sub-modules: attention rearrangement and attention reinforcement. To understand their individual contributions to performance, we conduct more detailed ablation study in Table It can be observed that removing attention rearrangement leads to a significant performance drop, further demonstrating that the more critical aspect of TS-Attn is the temporal redistribution of cross-attention distributions. Relying solely on attention reinforcement reduces TS-Attm to a mere attention enhancement mechanism for event tokens, lacking temporal correspondence. Combining both modules enables antensity-ddaptive attention allocation and achieves optimal performance.
E COMPARISON OF DIFFERENT TEMPORAL SEGMENTATION METHODS
We compare different temporal segmentation strategies that can be employed in TS-Attn.
Uniform Segmentation. This represents the simplest method for temporal segmentation: based on the number of events in the prompt, the video tokens are evenly divided into a corresponding number of intervals. In this setup, multiple events in the prompt are parsed by GPT-4o-mini.
User Input. Users can customize the intervals for each event based on the event count. For exam- ple, for prompt containing four events, the video tokens can be partitioned in ratio of 20%, 20%.
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System prompt
You are video analysis assistant. Your task is to divide the total video duration into time ranges corresponding to each event described in a given list of events. When performing this task, consider real-world physical constraints well as the subject performing these events. The output should be list of time ranges (as fractions of the total time) for each event, ensuring they sum to 1.0.
Specifically, given prompt, you should first extract the subjects and motion components from it, then reasonably allocate intervals based the order of events. Finally, please return JSON file that hierarchically organizes these events in temporal sequence. Below is an example:
In-context example
&
"A dog chases ball and barks, while cat naps on the couch and stretches."
"PROMPT":"A dog chases ball and barks, while cat the couch and stretches",
"dag": ("motion': ("chases ball", "barks"], "event_range" '(0.0-0.5, 0.5-1.017 00')),
"cat": ("motion": ("naps on the couch", "stretches"). "event_range": "[0.0-0.5, 0.5-1.0J3
Figure 8: The prompt template for temporal segmentation using the LLM API.
30%, and 30% to align with each event. In the experiments summarized in Table 8 we manually an- notated temporal intervals for each prompt in SerryEvvl--Beedee based on commonsense knowledge.
Efficient Planning with LLM API. This approach is similar input: instruct the LLM to partition reasonable temporal intervals for each prompt. Specifically, we employ the GPT--4omini for this segmentation task due its simplicity. The L.MM API processes each prompt in approxi- mately 2 to seconds, demonstrating high efficiency.
All three methods mentioned above are straightforward and easy implement. As demonstrated in Table 8, their differences in final performance are minimal. This further confirms that even with only coarse temporal interval guidance, TS-Attn is capable of achieving temporal-aware multi-event video generation. Moreover, overlapping intervals between different events do not significantly impact performance, as TS-Attn employs a soft attention redistribution mechanism. Video tokens within specific temporal interval are guided to focus primarily on attention interactions corre- sponding to their assigned event, rather than being completely isolated from other events. The prompt template used is shown in Figure g
MORE ATTENTION VISUALIZATION RESULTS
We present additional attention analysis to further elaborate the insights of AStAttn. As shown in Figure? the attention distributions of different actions in TS-Attn are clearly separated along the temporal sequence. Meanwhile, each event exhibits strong response the motion regions of its corresponding frames. This significantly enhances the temporal awareness of the original cross- attention and, expected, results in videos that respond accurately to all actions.
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Prompt: child builds a sandcastle, and then jumps on it to flatten
+TS-Attn
Wan2.2-A14B
-builds
-jumps
"flatten"
Attention map along the temporal sequence
Figure 9: More comparison of attention maps along the temporal sequence between TS-Attn and vanilla cross-attention.
G MORE QUALITATIVE RESULTS
In this section, we provide additional qualitative comparisons to further demonstrate the effective- ness of our method on multi-event video generation tasks. Figures [0guresTO[1 13 present more text-to-viden (T2V) cases under complex temporal prompts, where our approach consistently achieves coherent event transitions and maintains high visual fidelity. These results highlight the generalization ability of our model in handling diverse multi-event scenarios across different subjects and environments.
Moreover, Figures 16 and 17 showcase comparisons with Wan2.1-141 Our method demonstrates stronger temporal consistency and better adherence to prompt semantics, especially in cases involv- ing multiple interacting events. These results further validate the robustness and scalability of our approach beyond standard benchmarks.
H MORE COMPARISON WITH MULTI-PROMPT METHODS
VideoTetris Tian et al. (0044) and TALC Bansal et al. 2024) are frameworks that use multi prompt strategies address compositional generation and multi-scene generation, which share certain simi- larities with multi-event generation. To further expand our evaluation scope, we extend these frame- works to the multi-event generation task. Specifically, we implement VideoTetris and TALC on Wan2.2-A14B using the optimal hyperparameters specified in their original papers, ensuring fair comparison with TS-Attn. As shown in Table 9 TS-Attn substantially outperforms both TALC and VideoTetris. TALC's striet conditioning of each segment ence, leading to reduced performance. Although VideoTetris combines weighted global and local cross-attention, its lack of training distorts the original video latent distribution, resulting in quality degradation and minimal improvement. Qualitative visual comparisons are provided in Figure 18
uub-rrompts disrupts global coher-
I MORE DIVERSE APPLICATIONS OF TS-ATTN
In this section, we present more potential application scenarios of TS-Attn, including multi-event generation involving multiple subjects, scene-level multi-event generation, and enhancing the po- tential for interactive long-video generation.
Multi-subject multi-event generation. As shown in Figure 19 multi-event generation involving multiple subjects can be achieved by using attention rearrangement to dynamically bind each subject to its corresponding event in the temporal sequence while suppressing interference from other events.
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In this way, TS-Attn greatly enhances the model's capability to handle complex spatial and temporal prompts.
Scene-level multi-event generation. Meanwhile, also observe that Tt-Attn can handle scene- level multi-event transitions, such as landscapes and video styles (Figure 20). It accurately interprets dynamic temporal changes, responds precisely weather and artistic styles each temporal seg- ment, and smoothly completes the transitions.
Interactive long video generation. The Wan model typically supports generating videos of up to 5 seconds in length, which limits the number of events it can reasonably express to no more than 5, To handle more events, we applied TS-Attn to the recently proposed LongCat-Vidde Video-13.66 model [Team al. (2025). ) which natively supports video continuity. This enables to distribute larger number of events across multiple clips. For example, 9 events can be divided into 3 clips for generation while maintaining temporal consistency.
As illustrated in Figure 21 TS-Attn improves temporal awareness within each clip, greatly enhanc- ing the capability handle videos with large number of events. The benefits of integrating TS-Attn into architectures like LongCat- Video are twofold: 1) For a fixed number of events, TS-Attn enables generation with fewer clips; For fixed number of clips, TS-Attn effectively manages more intri- cate temporal descriptions. These results highlight the potential of TS-Attn for both interactive and long-form video generation.
THE USE OF LARGE LANGUAGE MODELS
We use large language models (LLMs) solely for polishing sentence structures and refining the lan- guage throughout the manuscript. All core aspects of this research, including central ideas, experi- mental design, data analysis, result interpretation, and conclusion derivation, are conducted entirely by the authors. The LLM serves only as auxiliary tool and is not involved in any key aspects requiring academic judgment or creative intellectual input.
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Table 5: Evaluation results on T2V tasks with LLaVA-OV-Chat-72) verifier. Best scores are bolded.
Model
Human
Animal
Object
Retrieval
Creative
Easy
Hard
Average
Closed-Source Model
Hailuo
48.0% 41.9%
40.1%
35.6%
51.7% 41.7%
19.5%
58.3% 56.1%
17.1%
41.0%
Kling-1.5
46.0%
35.1%
30.8%
24.1%
41.7%
Open-Source Model
Open-Sora-Plams 1.3.0
13.5%
13.2%
9.6%
17.1%
6.9%
28.3%
2.2%
12.7%
Open-Sora 1.2
26.2%
22.2%
20.2%
32.2%
15.4%
37.8%
10.8%
23.6%
Vchitect-2.0)
33.4%
30.5%
33.6%
33.6%
20.5%
51.4%
19.1%
31.6%
Pyramid-Flow
23.6%
20.0%
15.8%
26.4%
10.5%
38.1
4.5%
20.3%
SkyReels-V2
47.6%
47.2%
40.6%
56.9%
33.2%
60.5%
30.1%
45.9%
MAGI-1-24B
45.4%
38.8%
38.6%
48.7%
25,2%
55.8%
26.2%
41.2%
MEVG W442.2-A14B
55.4%
46.2%
45.2%
55.8%
33.9%
58.7%
35.0%
48.5%
DiTCtrl + Wan2.2-A14B
56.6%
54.5%
45.0%
59.9%
33.0%
65.2%
37.1%
51.8%
Cog VideoX-SB
19.7%
20.7%
17.4%
27.2%
8.1%
37.6%
7.1%
19.9%
+Ours
32.4%
29.8%
25.7%
39.9%
18.5%
48.2%
15.8%
29.6%
W-n2.1-1.3B
37.6%
37.7%
27.1%
33.6%
21.5%
45 6%
26.4%
34.4%
+Ours
46.2%
42.6%
36.5%
51.3%
28.5%
50.9%
33.3%
41.8%
Wan2.1-14B
51.0%
40.6%
36.4%
58.2%
23.8%
57.3%
31.6%
43.5%
+Ours
60.4%
55.9%
50.6%
67.6%
40.3%
73.7%
41.3%
55.9%
Wan2.2-A14B
62.6%
56.5% 63.4%
48.8%
70.2%
42.9%
69.3%
45.9%
56.8%
+Ours
70.6% %
58.0%
76.6%
48.9
78.0%
50.2%
63.9%
Table 6: Evaluation results on I2V tasks with L.aVA-OV-Chat-723 verifier. Best/2nd best
scores are bolded/underlined.
Model
Human
Animal
Object
Retrieval
Creative
Easy
Hard
Average
Framepack-138
41.4%
37.3%
35.4%
50.1%
25.0%
51.8%
28.0%
37.
SkyReels-V22 V2-114B
49.8%
44.5%
40.7%
52.7%
30.8%
54.7%
32.5%
43.8%
MAGI-1-24B
43.6%
38.3%
39.0%
46.2%
25.1%
49.7%
36.1%
40.4%
Cog CogVideoX-12V-5R
24.7%
24.6%
20.5%
29.3%
12.4%
42.9%
9.8%
23.9%
+Ours
37.9%
36.4%
30.8%
43.0%
20.2%
52.2%
22.1%
35.3%
Wan2.1-12V-14B
48.6%
38.4%
36.0%
53.2%
26.7%
53.0%
28.2%
41.4%
+Ours
53.0%
46.8%
42.8%
57.9%
32.3%
55.3%
39.0%
47.9%
Wan2.2-12V-A14B
56.7%
51.0%
47.6%
61.6% 66.8 %
35.3%
62.2%
41.3%
52.0%
+Ours
59.7% %
58.8%
51.8%
40.2%
67.5%
45,4 %
57.6%
Table 7: Ablation experiments on the subcomponents of Event-aware Attention Modulation.
Method
Wan2.2-A14B
CogVideon VideiX-oXS5
Easy
Hard
Avg
Easy
Hard
Avg
w/o Attention Rearrangement
63.1%
36.8%
49.4%
38.2%
18.8%
w/o Attention Reinforcement
67. 67.4%
41.2%
53.5%
41.8%
8.4%
23.6%
TS-Attn
70.5%
44.3%
56.2%
45.7%
9.9%
25.8%
19

---

Published as a conference paper at ICLR 2026
Table 8: Ablation results of different temporal segmentation methods.
Method
Wan2.2-A
Cog VideoX-SB
Easy
Hard
Avg
Easy
Hard
Avg
Uniform Segmentation
69.8%
42.6%
55.3%
44.5%
9.2%
25.2%
User Input
71.4%
45.0%
56.8%
44.8%
11.3%
26.5%
GPT-4o-mini Plan
70.5%
44.3%
56.2%
45.7%
9.9%
25.8%
Table 9: More multi-event T2V comparison with multi-prompt methods using GPT-4o verifier.
Best scores are bolded.
Model
Human
Animal
Object
Retrieval
Creative
Easy
Hard
Average
--2222-T2V-A14B
51.2%
46.7%
44.9%
54.8%
34.8%
60.3%
34.0%
48.3%
TALC
50.9%
45.4%
44.1%
56.2%
33.8%
60.6%
31.9%
47.1% 1%
VideoTetris
53.0%
46.5%
46.8%
63.6%
35.9%
63.5%
37.5%
49.7%
Ours
60.4%
53.6%
52.0%
63.0%
45.3%
70.5%
44.3%
56.2%
[1,0,0]
Moxsate
[1,1,00
Wont AL44
[1,1,0]
[1,0,0]
DITCH
[1,1,1]
An elevator door opens, dog goes out of the elevator, and then the door cleses.
Figure 10: More qualitative comparison results on multi-event generation.
[1.1.0]
MAD3300
[1,0,0]
(1,0,,1
[1,0,0]
ARte
mon walks on the dark street, snops fingers, and the streetlights turn on simultaneously.
Figure 11: More qualitative comparison results multi-event generation.
20

---

Published as a conference paper at ICLR 2026
[0.0]
Misisatt
[1,0]
Wost A-A
[1,0]
(1.0)
[1.1]
A sprinkler rotates, sproys water over the lown, and then stops.
Figure 12: More qualitative comparison results on multi-event generation.
[0.1]
[0,0]
[0.0]
[1,0]
[1,1]
A spider spins web between two flowers and then hides behind one of the flowers.
Figure 13: More qualitative comparison results on multi-event generation.
21

---

Published as a conference paper at ICLR 2026
Micisatt
[1,0,0]
Wont 2-A
[1,0.0]
[0,0.0]
A bicycle pedals itself down the street, stops of red light, and then continues when turns
Figure 14: More qualitative comparison results on multi-event generation.
[0,0,0)
[1,0,0]
[1.0,1]
[1.1.1]
spider hangs from ceiling, drops down, and then climbs back
Figure 15: More qualitative comparison results on multi-event generation.
22

---

Published as a conference paper at ICLR 2026
Wone 188
[0.1,0]
[11,11]
beever carries the branch, puts them in the middle of river, and then storts building a
[0,0,0]
Won 1444
[1,1,1]
+5-3m
bee files around flowers, and stops on one flower, and begins to collects pollen.
Wwn... 548
[1,3]
75:4m
bicycle competes race, furiously, and then finish first.
Wonz. 1448
[0.1]
[1,1]
bird sings on branch, and then flies away into the sky.
Figure 16: More qualitative comparison results with Wan2.1-14B.
23

---

Published as a conference paper at ICLR 2026
Wanz 188
[0.1]
0.11
+5 A5--
A boy clops his hands, and then all the leaves fall off the trees at once.
Won 144
[1.1]
bey football,
hoop.
Wont 344
10,1,0]
[0,1,1]
+5-3mm
butterfly lands
woman, moment, away.
[0,0,0]
Want 1886
[,1,11]
+15 Amt
up skyscroper, top, and drives off into the sky.
Figure 17: More qualitative comparison results with Wan2.1-14B
Wonz 2AA
[1,1,0]
[0.0.0]
TTMC
[1,0,0]
10.1.1)
1,1,13
A husky jumps into rocking chair, starts rocking it, and then gets thrown out of it.
Figure 18: More qualitative comparison results with multi-prompt methods.
24

---

Published as a conference paper at ICLR 2026
drg bull and borks, wide cat the ceac and stratches.
person lcoves and car entera
BALJ
mumiin steps
noning
shops
Video Teken
Text Token:
Figure 19: More qualitative results on multi-event generation with multiple subjects. The mask diagram the right side the figure briefly illustrates how attention rearrangement regulates the temporal attention intensity of each subject to different events under each prompt.
0..01.00
Wanzz 200.2.A2 A148
B.1.1.11
plain shifting
thunderstorm, by rainbow, finally glowing
[1.0.0)
Ros0a 2 AAA
eape SL
shifts gothic style glowing neon cyberpunk style, pointing style.
Figure 20: More qualitative comparison results scene-level multi-event generation.
25

---

Published as a conference paper at ICLR 2026
+TS-Attn
LongCat-Video-1B.68
(1,1,1]
10.1.J]
A sits desk, his laptop, typing the keyboard, and think for while.
[1,1,]]
2 He then grab glass of water, takes sip, and then looks over some papers beside him.
[0.1,0]
The adjusts the gesture, checks the paper, and finally closes the loptop.
Figure 21: More qualitative comparison results on interactive long video generation.
26


Reviews:
[{"review_id": "zraFI0YaWq", "reviewer": "Reviewer_HkQf", "summary": "the paper proposes a training-free temporal-wise separable attention for multi-event conditioned video generation", "questions": "please refer to the weaknesses section", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- the motivation of the work is clear and well-explained. the proposed motion region extraction and event-based attention modulation are intuitive and visualizations are reasonable\n- extensive experiments demonstrate the effectiveness of the proposed method. the visual results are convincing and clearly show the multi-event coherence\n- the method achieves reasonable performance improvements with negligible overhead\n- the paper provided source codes in the supplementary material", "weaknesses": "- the benchmark protocol and the evaluation metrics are not well-justified. while I understand there is no reasonable benchmark framework in the current field, the reliability of the vlm-based evaluation is still questionable, especially when evaluated against a commercial endpoint. in that case, it is hard to determine the actual performance gain based on the reported scores. a user study is highly recommended to validate the effectiveness of the proposed method considering the human evaluation is still the most reliable metric for video generation tasks\n- it is unclear what is the max possible number of events the proposed method can handle\n- what is the success rate of a given prompt? what are the typical failure cases?\n- while the proposed framework provides a solution for multi-subjects, it is unclear whether the proposed framework can faithfully handle multiple subjects with same/similar actions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KfMclvwXca", "reviewer": "Reviewer_Qohm", "summary": "This paper proposes TS-Attn, a training-free attention mechanism that improves multi-event video generation by dynamically separating and modulating cross-attention between motion regions and multi-event textual conditions. By introducing motion region extraction and event-aware attention modulation, the method reduces temporal misalignment and cross-event coupling, achieving better temporal coherence and event accuracy. TS-Attn can be plugged into existing diffusion-based video models without retraining, yielding substantial performance gains on StoryEval-Bench with only ~2% extra inference cost.", "questions": "See weakness. Overall, I lean towards borderline for the current version and am happy to update my rating if my questions are well answered.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Well-motivated and intuitive idea that directly targets temporal attention entanglement in multi-event video generation.\n\n- Training-free and plug-and-play design makes it broadly applicable across existing diffusion models.\n\n- Extensive experiments and ablations demonstrate consistent improvements and robustness across architectures and benchmarks.\n\n- Clear presentation and visualizations that effectively explain both the mechanism and empirical benefits.", "weaknesses": "**Lack of comparison with prior methods**\nThe paper omits several highly relevant works that also manipulate cross-attention maps to achieve fine-grained event grounding without retraining, such as DreamRunner [1], VideoTetris [2], and TALC [3].\nThese methods similarly align textual tokens with corresponding visual regions through attention reweighting, making them conceptually close to TS-Attn. However, the authors neither cite nor compare with them. Including these approaches as baselines or at least discussing their differences would strengthen the paper’s positioning and contribution clarity.\n\n**Unclear generalization to multi-subject scenarios**\nThe proposed motion-region extraction appears to assume a single dominant subject, computing masks for the entire video latents.\nIn cases involving subject transitions (e.g., a person leaves and a cat enters), this design may fail to isolate subject-specific motion regions, leading to incorrect or conflicting event-to-visual grounding.\nClarifying how TS-Attn handles such cases, or showing examples involving multiple subjects, would improve the completeness of the work.\n\n---\n[1] DreamRunner: Fine-Grained Compositional Story-to-Video Generation with Retrieval-Augmented Motion Adaptation, 2024.\n\n[2] VideoTetris: Towards Compositional Text-to-Video Generation, 2024.\n\n[3] TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zpPoRItams", "reviewer": "Reviewer_3mMY", "summary": "This paper proposes Temporal-wise Separable Attention, a training-free method to improve video generation with complex multi-event prompts. The proposed attention mechanism enables this by dynamically restructuring cross-attention distributions to ensure motion-related regions in each frame primarily attend to temporally aligned events, demonstrating superior results across various video generation models.", "questions": "Are there any issues with sudden scene changes?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The problem is well-defined and the solution is intuitively designed. The visualization in Figure 2 makes this more convincing.\n- The proposed Attention Rearrangement and Attention Reinforcement are carefully designed to better inject multi-prompts while maintaining pre-trained generative priors in a training-free manner.\n- The experimental results are extensive. The proposed method has been implemented on various pre-trained models to demonstrate its effectiveness and is also compared with other methods for multi-prompts.", "weaknesses": "- There are some heuristics arising from the training-free design. For example, the erosion function and the accompanying spatial separation of subject tokens limit the applicable scenarios of this method to simple ones. For instance, cases where the subject is the style of the video or cannot be clearly distinguished in 2D spatial aspects fall outside this premise.\n- While the attached video results look good, there is an absence of video comparisons with other multi-prompt methods. More qualitative comparisons in the paper would also be beneficial.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yjrApPHnl0", "reviewer": "Reviewer_oX7g", "summary": "This work presents advancements in a typical issue presented by video generation methods, which often produce sequences of events that present temporal anomalies like overlapping events and incorrect ordering. This problem is also discussed in detail, to better understand its causes and justify the approach.\n\nThe solution presented is TS-Attn, a method that requires a modification to the cross-attention layer such that it makes use of event ordering information presented in the prompt itself. This expanded attention layer  requires additional input of temporal segmentation information, which is generated using either an external API, human data, or a simpler segmentation method.\n\nFinally, the work does very complete experiments which (except for the caveats in the weaknesses section) appear to produce an important improvement to the provided baselines, while being a relatively simple to implement addition to the video generation systems.", "questions": "My main questions are about the segmentation pipeline and full impact on latency, which are discussed in detail in the weaknesses section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "The paper has substantial strengths:\n\n1) the problem is worth solving, and the root cause analysis is excellent\n2) the presented method is (to the extent of my knowledge) novel and interesting; event-aware attention modulation in particular seems like an interesting approach to me\n3) the benchmark is very complete and required the integration to 3 different models, which is impressive\n4) the benchmark results that are indicated in the paper are very strong, except for the points raised in the weaknesses section which will hopefully be easy to fix\n5) finally, the paper is well written, and the explanation is very clear", "weaknesses": "This work presents a very complete analysis of an original method.\n\nThe soundness and contribution scores of this paper are diminished by ambiguity on the impact of latency in the full system, as explained below.\n\nSpecifically, the work would benefit from a more detailed explanation of how the uniform segmentation method works. When explaining this method, the paper refers to a number of events in the prompt, but it's unclear how the events in the prompt are parsed themselves. To me this seems like a critical point because if the event segmentation in the prompt has to be provided by a model for example, the time to run such segmentation should be accounted for in the latency analysis.\n\nThis applies generally to claims about latency, for example in figure 1c, and in other tables in the paper. It's unclear to me which segmentation method was used for each figure and table, whether segmentation itself was included in the latency analysis or not, and whether TS-Attn required additional human input that other methods didn't require (e.g. if uniform segmentation required manual selection of prompt events).\n\nIn summary, the paper could benefit from:\n\n1) more clearly specifying how the uniform segmentation method works\n2) providing details on which segmentation method was used for each figure and table\n3) providing details on the impact of segmentation on latency\n\nMy understanding is that in most figures the LLM API approach is used, and it only adds about 2-3 seconds to latency (changing the +2% latency claim to 2.2% or 2.3% perhaps), but it would be good to be more explicit about this.\n\nMore minor points:\n\n4) including the prompts used in the segmentation methods in the appendix could be an interesting addition.\n5) that figure 2 makes use of 3 colors, 2 of which can be easily confused with each other.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)