Anchor Frame Bridging for Coherent First-Last Frame Video Generation
Authors: Xuehan Hou
Meng Fan
Pengchong Qiao
Zesen Cheng
Yian Zhao
Lei Zhu
Kaiwen Cheng
Chang Liu
Jie Chen
Venue: ICLR 2026 Poster (2026)
Keywords: First-Last Frame Video Generation
Primary area: generative models
TL;DR: Anchor Frame Bridging for Coherent First-Last Frame Video Generation

Abstract: First-last frame video generation has recently gained significant attention. It enables coherent motion generation between specified first and last frames. However, this approach suffers from semantic degradation in intermediate frames, causing scene distortion and subject deformation that undermine temporal consistency.
 To address this issue, we introduce Anchor Frame Bridging (AFB), a novel plug-and-play method that explicitly bridges semantic continuity from boundary frames to intermediate frames, offering training-free adaptability and generalizability. By adaptively interpolating anchor frames at temporally critical locations exhibiting maximal semantic discontinuities, our approach effectively mitigates semantic drift in intermediate frames. Specifically, we propose an adaptive anchor frame selection module, which generates text-aligned candidate frames via frame order reversal and selects anchors based on semantic continuity. Subsequently, we develop anchor frame guided generation, which leverages the selected anchor frames to guide semantic propagation across intermediate frames, ensuring consistent boundary semantics and preserving temporal coherence throughout the video sequence. The final video is synthesized using the first frame, last frame, selected anchor frames, and the text prompt.
 The results demonstrate that our method significantly enhances the temporal consistency and overall quality of generated videos. Specifically, when applied to the Wan2.1-I2V model, it yields improvements of 16.58\% in FVD and 10.21\% in PSNR. The codes are provided in the supplementary material.

Published as a conference paper at ICLR 2026
ANCHOR FRAME BRIDGING FOR COHERENT FIRST- LAST FRAME VIDEO GENERATION
Xuehan Hou' Meng Fan 1.4-
Fan
Pengchong Qiao' Zesen Cheng'
Yian Zhao'
Lei Zhu" Kaiwen Cheng Chang Liu Jie 2,,
1 School of Electronic and Computer Engineering, Peking University, Shenzhen, China
2 School of Intelligence Science and Engineering, Harbin Institute of Technology. Shenzhen, China
3 Pengcheng Laboratory, Shenzhen, China
Al for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China
Department of Automation and BNRist, Tsinghua University, Beijing, China
ABSTRACT
First-last frame video generation has recently gained significant attention. en- ables coherent motion generation between specified first and last frames. How- ever, this approach suffers from semantic degradation in intermediate frames, causing scene distortion and subject deformation that undermine temporal consis- tency. To address this issue, we introduce Anchor Frame Bridging (AFB), a novel plug-and-play method that explicitly bridges semantic continuity from boundary frames to intermediate frames, offering training-free adaptability and generaliz- ability. By adaptively interpolating anchor frames temporally critical locations exhibiting maximal semantic discontinuities, our approach effectively mitigates semantic drift in intermediate frames. Specifically, propose adaptive chor frame selection module, which generates text-aligned candidate frames via frame order reversal and selects anchors based on semantic continuity. Subse- quently, we develop anchor frame-guided generation, which leverages the selected anchor frames to guide semantic propagation across intermediate frames, ensur- ing consistent boundary semantics and preserving temporal coherence through- out the video sequence. The final video is synthesized using the first frame, last frame, selected anchor frames, and the text prompt. The results demonstrate that our method significantly enhances the temporal consistency and overall quality of generated videos. Specifically, when applied to the Wan2. model, it yields improvements of 16.58% FVD and 10.21% % PSNR.
INTRODUCTION
Video generation models have shown remarkable proficiency in generating high-quality videos and support multiple generation paradigms, including text-to-video generation Hong et al. (2023); Vil- legas et al. (2023); Singer et al. (2023); OpenAl (2023); Zhang et al. (2024); Li et al. (2025); Yang al. (2025h), image-to-video generation Ho et al. (2022); Blattmann al. (2023); Peng et al. (2024); Yang et al. (2025a); Wang et al. (2025b); Namekata et al. (2025) However, the first-last frame video generation Wang et al. (2025a); Bytedance (2024) remains a valuable yet underex- plored area, which involves creating a video that simulates consistent motion between the first and last frames. Given its unique characteristics, an effective first-last frame video generation method can serve powerful tool in many applications, such video editing, physical modeling, etc.
Although it is certainly possible to train large-scale video generation models to accept specific frames as conditions, training such a model requires too much time and economic cost. Most ex- isting methods Bytedance (2024); Wang et al. (2025a) realize first-last frame video generation by reusing image-to-video models, which use both first and last frames as control conditions. Despite some success, these methods still suffer from information attenuation intermediate frames. That is, the deterministic semantics of the first and last frames gradually decrease they propagate the
"quual contribution. Corresponding author.

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Published as a conference paper at ICLR 2026
FLF2V
AFB (our method)
L trana
fist trame
018
last frame
Frame
Frame
Intermediate frame attention visualization.
LPIPS similarity belween the firstlast frames
Eri Frars
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Text Gare
Input
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PLIZY MODEL
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Iree Inur
bast EiTMA
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Isst trame
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(c) Vanilla method
(d) Our method AFB
Figure 1: Anchor Frame Bridging (AFB) design. (a) Visualization of inter-framm attention in video generation via DiT's self-attention (left). Visualization of attention from the first-last frames to each intermediate frame (right). Notably, only adjacent frames exhibit substantial inter-frame attention values, while the first and last frames show relatively low attention to the middle frames, suggesting that the deterministic semantics in the first-last frames gradually diminish as information flows toward the intermediate frames. (b) Under the original method, LPIPS similarity between the first and last frames drops suddenly in middle frames. Our AFB shows smooth similarity change without such a drop. (c) Vanilla method's generated videos exhibit poor middle-frame quality. (d) Our AFB method smooths middle frames and enhances video temporal consistency.
intermediate frames. This is because, in self-attention layers, only adjacent frames have significant inter-frame attention values, causing lower attention weights from the first/last frames the inter- mediate ones, as shown in Fig. (a). This results in poor temporal consistency in the final video. As illustrated 1 frames near the first frame show higher generation consistency, while those in the mid-to-late part of the sequence show reduced coherence. Moreover, to maintain continuity with the terminal frame, the last few frames abruptly adopt the ending frame's ss attributes, causing abrupt transitions and unstable temporal dynamics throughout the video sequence.
To this end, we propose novel Anchor Frame Bridging (AFB) method to bridge the semanties of the first and last frames to the intermediate frames. By strategically interpolating anchor frames points of continuity breaks in generated vidcos, this method effectively reduces mid-sequence frame deviations and enforces controlled trajectory alignment throughout the generation process, as shown in Id). Our AFB operates through two core steps: adaptively selecting an appropriate anchor frame from candidate set, and then utilizing guide the generation of the final video.
Specifically, the adaptive anchor frame selection module is used to obtain anchor frames. As the low quality of intermediate frames adversely affects subsequent frames, the initial video segments maintain higher quality, whereas coherence breakdowns typically occur in the mid-to-late sections. To address this issue, we reverse the order of the first and last frames, thereby obtaining higher- quality candidate set for anchor frame selection. We detect temporal discontinuities in the generated video and denote the position of the most significant break as a. Empirically, such breakpoints oceur at nearly symmetric relative positions in forward and reverse generation (Appendix E). Accordingly, we select the anchor at the mirrored position ---a O..
After obtaining the anchor frame, we present the anchor frame guided generation to guide the generation process using the first frame, last frame and anchor frame. The anchor frame inserted at the position of the vidco. For the given frames (first, last and anchor frames), apply binary mask and concatenate features from the first/last frames with textual features before feeding them into the model. For other frames, directly feed them into the model. This approach ensures the temporal consistency and coherence of generated video maintains.
Given the lack of datasets for larst-last frame video generation, we first construct a dataset comprising 436 pairs of first and last frame images along with corresponding text prompts. We then conduct both qualitative and quantitative analyses this dataset. We test our method two common open source
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Published as a conference paper at ICLR 2026
12V models: Wan2.1 and Hunyuan Video and conduct comparative experiments alongside Wan2.1- FLF2V Wang et al. (2025a), ViBiDSampler Yang et al. (2025a), and Generative Inbetweening Wang et al. (2025b). The results show that method consistently outperforms the original baselines, achieving improvements of 16,58% in FVD and 10.21 % in PSNR compared to the Wan2.1-12V Our main contributions are follows:
We introduce Anchor Frame Bridging (AFB), a method that bridges semantic continuity be- tween first-last frames through anchor frame interpolation, mitigating intermediate distortion and resolving temporal inconsistency in first-last frame video generation.
AFB is a plug-and-play method that operates in two stages: adaptive anchor frame selection via temporal continuity analysis and anchor soneegguied generation for coherent videos.
We provide high-quality first-last frame video generation dataset and conduct extensive experi- ments on it. Results show that our method significantly outperforms existing methods, achieving 16.58% improvement in FVD and 10.21% % PSNR.
2 RELATED WORK
Image-to- Video Diffusion Models, Image-to- Video Generation (12V) focuses on generating dy- namic video sequences from input images guided by text prompts. This approach enhances the con- trollability of video generation by anchoring the output to a specific first frame. Existing methods, such as 12VGen-X0 XL Zhang et al. (2023), SVD Blattmann et al. (2023), Hunyuan Video-I2V Wei- jie Kong et al. (2024), VogVideoX Yang et al. (2025b) and Wan2.1-12y Wang et al. (2025a) extend the text to video (T2V) framework 12V by concatenating conditional latent representations with noise latents. These methods leverage the robust prior knowledge embedded in the underlying T2V models to achieve high-quality 12V generation. However, the incorporation of this prior knowledge tends to render the generated videos more reliant text rather than input images. Frames initially resemble the input image, but gradually drift, which does not satisfy user requirements.
Video Frame Interpolation. Video frame interpolation aims to generate appropriate intermedi- ate frames from two boundary frames. Flow-based methods Park et al. (2020); Kong et al. (2022); Huang et al. (2022); Li et al. (2023); Hu et al. (2024); Zhang et al. (2025) explicitly estimate op- tical flow or leverage flow guidance but struggle with complex extreme motions. Diffusion-based methods have also been explored in several studies. Voleti et al. (2022); Danier et al. (2023); Jain al. (2024); Shen al. (2024). Some methods employ time-reversal strategy that fuses forward and reverse denoising paths to ensure adherence to both first and last frames. However, when the first and last frames differ significantly, the forward and backward paths exhibit substantial dynamic discrepancies, causing undesirable artifacts in the fused video frames. To mitigate such discrep- ancies, existing methods often introduce additional costs, such as multiple noise reinjections Yang et al. (2025a), frame-level constraints Zhu et al. (2025) and extra model training Wang et al. (2025b). This leads to significant computational overhead, compromising the system's 's reasoning efficiency. In contrast, our method AFB, which leverages an adaptive anchor frame addition strategy, transmits the information of the first and last frames to the intermediate frames. This eliminates the need for complex fusion steps and yields more coherent results.
First-Last Frame to Video. First-Last Frame Video Generation (FLF2V) is novel video genera- tion task, which involves automatically generating coherent intermediate frames from given first-last frames and text prompts, enabling highly controllable and consistent video generation. Make Pixels Dance Zeng et al. (2024) first proposed a diffusion- model architecture, which is capable of generating videos guided by text and first-last frames, employing a strategy of concatenating the first and last frames with zero- pero-padding to create dynamic videos. Earlier this year, Wan2. 1-FLF2 Wang et al. (2025a) released a 14B-parameter FLF2V model, which concatenates the first and last frames with noise and feeds them into the diffusion model via conditional control branch. Current FLF2V methods, fine-tume nued from I2V models, suffer from weakened first-last frames semantics in interme- diate frames. To mitigate this problem, propose the Anchor Frame Bridging (AFB) to explicitly enhance the propagation of the semantics of the first and last frames, thereby significantly improving the consistency and video quality of first-last frame video generation.
3

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Input First Frame Last Frame
Adaptive Anchor
Reconstructed Input Frames
Anchor
Ouiput Final Video
Frame Selection
First Frumellasi Ancher Frunc
Guided Generation
(a) Adaptive Anchor Frame Selection
(b) Anchor Frame Guided Genenation
vanum Inamoiiss four
S>p Qwen
trauiiisus froms forward hend
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final video
Ive juucl
Mirao Podiss
Text Encoder
indicutor
Reverse
Encodor
D11
Decoddr
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Decoder
I-u
Encoder
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Figure 2: Method architecture. (a) Adaptive Anchor Frame Selection: Text prompts generated by Qwen, along with the swapped first and last frames, fed into the video generation model to produce the anchor frame candidate set. Then we use the adaptive anchor frame selection method select the anchor frame. (b) Anchor Frame Guided Generation: The first frame, last frame and anchor frame are concatenated with zero-padded frames along the time axis. The indicator guides the model's input conditions and specifies which frames will be generate. After denoising and decoding, we obtain the final video with high consistency to the first and last frames.
3 METHOD
To address the information attenuation in intermediate frames, propose AFB to incorporate the semantic information from the first and last frames into the middle part of the video by inserting anchor frames. As illustrated in Fig. 2, method consists of two main components. The adaptive anchor frame selection module first generates a candidate set of anchor frames and selects the most suitable one, which is then utilized by the anchor frame guided generation module produce the final video. Detailed descriptions of these two modules are presented in the subsequent sections.
3.1 PRELIMINARIES
Diffusion models learn the underlying data distribution To g(xo) by learning to reverse prede- fined, iterative process of noising data. For 12V diffusion models Blattmann et al. (2023), the process often operates the latent space pre-trained VAE encoder to reduce computational complexity, where a clean video sample zo x is first encoded into latent space as =o Exzo). A forward Markov process is then applied to gradually corrupts this latent representation into pure Gaussian noise over T steps:
((2:rr 20)
II g((z z(-1), q(z1 21-1) = N (zi: /1- (12-111,, 1=1
(I)
where 3e E (0, 1) is predefined noise schedule at time step The reverse process reconstructs clean latent from pure noise zr via iterative denoising, which can be formulated as:
2 update(zz. fo
where fe is a denoising network, and is the conditioning signal. In some flow-based model, the network is specifically parameterized as uo to predict a velocity field. After the iterative denoising, the final clean latent is decoded back to the generated video via VAE decoder.
3.2 ADAPTIVE ANCHOR FRAME SELECTION
To obtain anchor frames with high similarity to the first and last frames, we design the adaptive anchor frame selection module, which consists of two core stages, as shown in Fig. 2(a). First, reverse generation process is employed to create an anchor frame candidate set. Second, we apply an adaptive anchor frame selection strategy select the appropriate anchor frame from this set.

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Published as a conference paper at ICLR 2026
Reverse Generation Process. Given the first frame Io and last frame IN-1- swap their po- sitions and employ Qwen Bai et al. (2023) to generate a reverse-directioe prompt denoted as prey which captures the semantic and visual context of both frames and explicitly describes the transition dynamics between them,
..ev Qwen( N-1: To)-
(3)
This text prompt is subsequently encoded into a text embedding, denoted as cpe. The new initial frame -1 and final frame /o, encoded to form the conditional input ž,:
Zc = 11,
where the VAE encoder.
The conditional input že, along with text embedding Cpre, guides the model during sampling. The iterative denoising update can be formulated as
-
update( Zz: uz(zii Zo, (p
(5)
where us (zti t, Re, cpt) denotes the output velocity predicted by the model, and represents the timesteps.
We terminate the reverse denoising at stop step K. Since the latent 2 is noise-corrupted inter- mediate timesteps, we compute the predicted clean sample &o derived from the current step žy:
Z1 /1 (2,0(Z111)
20
Vāt
Decoding the predicted dn at stop step K yields sequence of predicted clean frames {In { which forms the candidate set for anchor selection. Let T be the total denoising steps, we perform reverse denoising for K S steps. K T corresponds the full reverse process, whereas K < T provides accelerated variant.
N-1
Adaptive Anchor Frame Selection. To identify a suitable anchor frame from this candidate set and determine its insertion position, we first define frame quality evaluation function Q to assess the generation quality of each frame I, in the candidate set. We evaluate the entire sequence to find the frame with the poorest quality, located index np:
np arg min Q(Ix))
where n E [0, N - 1]. This index corresponds to the normalized relative position a = 1nnnnpp/NN - 1), which marks the location of the most significant continuity breakpoint.
As discussed in Appendix E, the continuity breakpoint location is nearly symmetric between forward and reverse generation. To mitigate this information attenuation the corresponding position in the forward process, we replace the generated frame at location a with a high-quality anchor frame selected from the candidate set obtained via reverse generation. Since the reverse generation process swaps the first and last frames, the index of the anchor frame in the candidate set n. should follow a mirror positional relationship with a:
na = -)) a).
(8)
The frame is usually close to the first frame (the original last frame) of the reverse generation, which means is less affected by information attenuation, and typically exhibits high quality and strong semantic alignment. These characteristics make it an ideal anchor frame, denoted as Ta.
In this work, we measure frame quality based on LPIPS. Unlike metrics such as PSNR and SSIM, which perform pixel-level image comparison, LPIPS utilizes pre-trained deep neural network to more accurately simulate human visual perception. higher LPIPS value indicates greater percep- tual difference between adjacent frames, corresponding to poorer coherence in the video segment. Therefore, the peak values of LPIPS mark the transition points with the worst temporal coherence in the video sequence. Our final frame quality evaluation function Q is defined as the negative of the local average LPIPS for frame In:
Q(I,) - (LPIPS(4n-1: 4n) + LPIPS( In+1)).
(9)
Thus, large LPIPS value results in small " low quality for the frame T.-
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Published as a conference paper at ICLR 2026
3.3 ANCHOR FRAME GUIDED GENERATION
After obtaining the anchor frame, we feed the first frame Jo. last frame Ix-1 and anchor frame I, as conditions into the video generation model shown in Fig. To inform the model of the three frames we already have and prevent their regeneration, we design an indicator that specifies which frames need generation and which are already available. We leverage the reasoning capability of Qwen to derive a coherent text prompt piwd from the given first and last frame images:
piwd Qwen (T. NN-1)-
(10)
Subsequently, we extract the image features from the first-last frames and the text features Cprs from the generated prompt piwd, After combining these features, they are fed into the video generation model along with the indicator to guide the final video generation.
Different 12V models have different sampling methods. In this paper, we use the Wan2.1-12V to illustrate the specific implementation of our anchor frame guided generation module. In Wan2.1- 1- 12V, we use an additional binary mask frame as the indicator. We introduce a binary mask M {0. 10 1111)xxxxxx where indicates the frames to be retained and O) indicates frames to be generated. While the spatial dimensions of M match those of Z.s its temporal length the same as the target video. Then it is rearranged in shape of 8 n X h x w, where is the temporal span of the encoder. The first frame lo, last frame _N-1 and anchor frame Lg are concatenated with zero- sero-padded frames along the time axis to form the guiding frames Le E C xHxW These guiding frames are then compressed into the latent space using the encoder 2. = EI2.) and 2e E Roxxxix" with c = 16 representing the latent channels,
3 (N 3)/4 h H 88 and w W/8.
Additionally, we use the image encoder of CLIP Radford et al. (2021) to extract conditioning fea- tures Co and CN-1 from the first frame To and last frame TN These are then concatenated get a conditioning vector e;:
C: = [Co, CN- These features are then injected into the DiT et al. (2022) model through decoupled cross- attention. The final iterative denoising process is given by
(11)
- update(Zz, up (Zzz L, m, Ci2 Cptid, Z,);4),
(12)
where m represents rearranged masks, us (2+; L, m, Ci, Cpna, 2.) denotes the output velocity pre- dicted by the model. After certain denoising process, representation 2q encapsulates the semantic information from the first and last frames, guided by the anchor frame and conditional inputs, and is ready for further decoding produce the final video.
obtain clean latent žu- This latent
4 EXPERIMENTS
4.1 IMPLEMENTATION DETAILS
Baseline Models. To evaluate the effectiveness and generalizability of our method, we integrate into two state-of-the-art 12V frameworks: Wan 2.1 Wang et al. (2025a) and Hunyuan Video Wei- jie Kong et al. (2024). For comparison, include two categories of baseline methods: (1) the current state-of-the-art FLF2V model, Wan2.1-FLF22 Wang et al. (2025a); and (2) video frame interpolation approaches, including ViBiDSampler Yang et al. (2025a) and Generative Inbetween- ing Wang et al. (2025b).
Evaluation Metrics. For evaluation metries, use LPIPS Zhang et al. (2018) to evaluate in- dividual frame quality and FVD Unterthiner et al. (2019) for overall video quality. SSIM Wang al. (2004) and PSNR measure similarity between generated and original videos. Additionally, following SvoryEval Wang et al. (2024), we adopt MLLMs, GPT-4o OpenAl (2024) and Gemini 2.0 Flash Eric Bieber al. (2025), as additional evaluators to assess the overall video quality from two perspectives: inter-frame consistency and prompt matching degree, with scores up to 100.
'The constant in this equation represents the total number of control frames after inserting the anchor frame (ie., the first frame, the anchor frame, and the last frame). In scenarios utilizing multiple anchor frames for FI.F2V tasks, this equation generalizes to n = (Na + 2) + (N - (Na + 2))/4, where N, is the number of anchor frames.

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Published as a conference paper at ICLR 2026
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Figure 4: Qualitative evaluation. We compare Wan 2.1 and Wan2.1 + AFB across different scenar- ios. The first row of each group represents the results of Wan 2.1, and the second row represents the results of our method. The anchor frame inserted is highlighted with the red bounding box.
Table I: Comparison with bascline models.
Gemini 2.0
Baseline Models
LPIPS 4 FVD 4
SSIM PSNR + GPT-4o
Flash f
ViBiDSampler
0.19
426.15
0.90
33.08
82.06
82.88
Generative inbetweening
0.24
453.76
0.85
31.25
75.42
72.15
Hunyuan Video-I2V
0.25
496.32
0.82
31.48
73.28
71.69
Hunyuan Video + +ABB (Ours)
0.21
435.71
0.89
32.54
81.33
79.26
Wan2. 1--2V 1va2.11FLFP)
0.22
449.68
0.87
32.13
79.31
76.43
0.19
413.68
0.91
33.20
84.23
84.94
Wan2.1 +AFB (Ours)
0.16
375.12
0.97
35.41
88.64
89.35
Dataset Category Distribution
Dataset. At present, there is notable absence of high-quality, publicly available datasets specifically designed for the FLF2V task. To evaluate the performance of our AFB across different scenarios, we collect dataset of first and last frames encompassing various scenes such as TV series, indoor/outdoor scene and human poses from the DAVIS Pont-Tuset et al. (2017), the RealEstate 10K Salem et (2021) and publicly available videos online. We filter the collected videos to create a high- quality dataset featuring significant visual vari-
Famii
Coment
Anomal
Canuon
Indocr sctois
Siappsse
Natural seent
TV Seriee
Romance
Movie
Action
Ukbba
Lite Record
Por
Street
Beach
Food
Scone (26%)
(21%)
Noga
Fousbolt
Baskstalll
Badnisee
Daily
Sport (20%)
(185%)
ation between the first and last frames, resulting final collection of 436 videos and their responding frame pairs. The distribution of our dataset is shown the Fig.3.
TV Series (15%)
Figure 3: Distribution of dataset.
4.2 BASELINE COMPARISONS
Qualitative Evaluation. We present visual comparison between method and Wan2. 1-12V in Fig.4, showing the superior performance of our approach in terms of motion stability, consistency and overall quality. Under the same input conditions (first-last frames, text prompts), Wan2.1-12V shows issues in the middle frames of the generated video, such as scene distortion, chaotic human limbs and reduced subject consistency. These issues effectively mitigated with AFB. More-

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Table 3: Ablation study on stop step K.
Stop Step (K)
Time (min)
Overhead
FVD 4
LPIPS
SSIM
PSNR
GPT-4of
Geminit
5
23
+15%
412.73
0.19
0.90
32.74
82.53
81.64
27
+35%
388.45
0.18
0.93
33.68
85.32
86.
40
37
+85%
379.35
0.16
0.96
34.81
87.59
87,30
50
41
+105%
375.12
0.16
0.97
35.41
88.64
89.35
Table 4: Ablation study on text prompts.
Text prompts
Methods
LPIPS
FVD &
SSIM
PSNR
General Texts
Wan2.1
0.32
486.23
0.76
29.15
General Texts
Wan2. + AFB (Ours)
0.28
475.33
0.81
30.54
Text by Qwen
Wan2.1
0.22
449.68
0.87
32.13
Text by Qwen
Wan2.1 + AFB (Ours)
0.16
375.12
0.97
35.41
over, our approach is not only effective in real-world scenarios but also generalizes well to animated scenes, shown in the third row of Fig.4. More comparison results are available in the Appendix. B.
Quantitative Evaluation. We conduct comparative evaluations using dataset against baseline models. The results are shown in Tab. 1. It is evident that our AFB method substantially enhances the overall quality of video generation, along with the smoothness and consistency between the first and last frames, outperforming all original baseline models.
4.3 ABLATIONS
Effect of Multiple Anchor Frames. We
Table 2: Ablation study on multiple anchor frames.
ablate the number anchor frames (Na) to
N₂ values
N₂ = 1
N₂ = 2
N₂
study how additional constraints affect gen-
eration quality. Specifically, we use the qual- ity assessment function Q identify Na (where No E (1,2,3) 3)) frames with the low-
LPIPS 4
0.16
0.18
0.21
FVD 4
375.12
386.94
397.50
est quality scores in reverse sequence, and select their hiiroordd-psiitiee anchor frames. As shown in Fig. 2, under our default video length setting (5 s), using single anchor frame achieves optimal performance. This finding is further supported by our user study (see Appendix. H for details). We attribute the degra- dation with more anchors to weer-conttaiiinn the generation trajectory: multiple anchors may intro- duce competing constraints, which can reduce motion fluency and diminish generative diversity. In this setting, single anchor frame is sufficient to mitigate semantic decay while preserving genera- tive diversity, striking an effective balance. For longer videos, however, multi-anchor configurations be beneficial; see Appendix G for more details.
SSIM t
0.97
0.93
0.82
counterparts as
PSNR 1
35.41
34.27
30.49
Effect of Stop Step K. As described in Sec. 3.2, terminate the reverse denoising after K steps and perform anchor selection based on the resulting candidate sequence. This stop step K offers direct cificiency-quulity trade-off in 2FB Intuitively, smaller К reduces computation but provides less denoising and weaker visual cues, whereas a larger K yields more reliable candi- dates the cost of higher overhead. As shown Tab reducing the stop step significantly lowers time overhead. Notably, terminating at timestep 15 (f 15) achieves an optimal balance between performance and cost: the FVD 388.45, which very close to the full-pass AFB (375.12) and still significantly outperforms both the original Wan2.1-12V (449.68) and 1an2.11FLFFD (413.68), while the inference time increases by only 35% compared to the baseline (27 min vs. 20 min).
Except for the ablation in Table 3, all other experiments in the main paper use the full reverse denoising (K 7) to maximize generation quality. In practice, K can he adjusted trade off efficiency and fidelity.
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First frame
LPIPS
3.0
1,0
0.5
0.6
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02
Last frame
0.0
80
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20
Frame of
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PIPS
LPIPS
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1.0
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Last frame
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80
40
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b
bz
Figure 5: Inter-frame similarity analysis. For DAVIS-25 train-69 (Left), the initial frames show almost no change; however, starting from frame 40, sudden scene shift (motorcycle movement) indicates poor continuity in the intermediate frames. For RealEs-25 95 (Right), the scene switches abruptly to another between frames 40 and 70, demonstrating that the generation of intermediate frames is not controlled by the first and last frames.
First frame
Attention Decay
First frume
Enhanced Control
Anchor Frame
8
12
Last frame
Last frame
0.00
0.20
0.40
0.60
0.00
1.00
0.00
0.20
0.40
0.60
0.90
Figure 6: Visualization of the first and last frames' attention to each intermediate frame. The
left and right figures respectively show the effects before and after the application of our method.
Effect of Text Prompts. Tab.4 presents ablation study descriptions generated by Qwen Bai et al. (2023) (customized to align first-last frame semantics) with general prompt such "a nice video". The results show that our AFB outperforms 1a 12V Wang et al. (2025a) in both cases. Moreover, detailed textual guidance enhances the controlla- bility of video generation, meeting the requirements of iser-customized generation.
prompts, comparing specific
4.4 EMPIRICAL. STUDY
Analysis. To verify whether our proposed AFB effectively bridges the information from the first and last frames to the middle frames, we conduct analytical experiments. We randomly select some samples from our dataset and perform comparative similarity analysis on the video frames generated under first-last frame conditions versus with our AFB method (using the LPIPS metric, where higher values indicate lower similarity). As shown in Fig.5, our AFB successfully bridges the semantics of the first and last frames to the intermediate frames via anchor frames, thus enhancing the overall consistency of the vidco. In Fig. 6, we visualize and compare the self-attention the first and last frames the intermediate frames for AFB method and the original method. Before incorpo-

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rating the anchor frame, the attention maps for the intermediate frames exhibit significant sparsity. After applying our method, this phenomenon is markedly mitigated, which demonstrates that our AFB successfully bridges the semantics of the first and last frames to the intermediate frames via anchor frames, thus enhancing the overall consistency of the video.
5 CONCLUSION
First-last frame video generation suffers from semantic degradation in intermediate frames, causing scene distortion and subject deformation that undermine temporal consistency. To address this is- sue, we propose Anchor Frame Bridging (AFB), training- method that mitigates distortion by inserting an anchor frame to bridge the semantics of the first and last frames. Specifically, to obtain an appropriate anchor frame, we introduce an adaptive selection module that dynamically selects the proper anchor frame from candidate sets generated through reverse process. After that, we develop an anchor frame guided generation approach to generate final video. Experimental results verify that our AFB explicitly enhances the propagation of the first-last frame's ' semantics, thereby improving the consistency and overall quality of the generated video.
Discussion. Our method inherits limitations from the underlying 12V foundation models. While outperforming baseline models in extreme scenarios involving drastic viewpoint changes, severe occlusions and non-rigid deformations, our approach still exhibit motion distortion and physically implausible motion. We analyze these failure cases in the Appendix D.1. Looking forward, be- lieve these limitations will be mitigated as more powerful video generation models emerge. More- over, our analysis of semantic information decay and the proposed anchor-frame insertion strategy offers valuable insights for future research on temporal continuity in video generation, particularly for challenging tasks such as long-form generation with distantly spaced start and end frames.
ACKNOWLEDGMENTS
This work was supported in part by Natural Science Foundation of China (No. U24B6012, 61972217, 3207 1459, 62176249, 62006133, 62271465, 62406167), the Shenzhen Medical Research Funds in China (No. B2302037), the New Generation Artificial Intelligence-Nations Science and Technology Major Project (No. 20252D0122702), and AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, China.
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A THE USE OF LLMS
In the preparation of this manuscript, we leveraged Large Language Models (LLMs) to refine the text for improved clarity. Furthermore, we use LLM generate image descriptions which served as one of the control condition for video generation process, as detailed in Sec. 3, and to provide auxiliary evaluation metric for our experimental results detailed in Sec. 4.1.
B MORE VISUALIZATION RESULTS
The current FLF22 (First-Last Frame to Video) model exhibits several limitations when generating videos from given start and end frames, as shown in Fig. 7. These issues include unnatural and abrupt transitions paristioonspaticullllly toward the end of the eqquence-where the video appears to forcibly align with the provided last frame. Additionally, the model produces implausible limb distortions of the main character, which become especially noticeable during high-intensity motions. Other artifacts, such as ghosting, further degrade the visual quality and coherence the generated videos.
Mulation near the last frame
(b) third hand appears of nowhere
got dopp
got doss
(c) Extremely twisted limbs
Suddenly disappearing lext
Ghosting
Twisted limbs and ghosting
(g) Unnatural motion trajectory
(h) Strange
Figure 7: Disadvantages of generating videos.
The detailed experimental results are presented in Fig. 8. Each unit displays the provided first and last frames along with the corresponding text prompts the left. On the right, the top and bottom rows show the video sequences generated without and with our method, respectively. A comparison between the two rows clearly demonstrates that AFB module substantially enhances the alignment with the given start and end frames. For instance, in the third example, distorted body movements present in the original output effectively corrected after introducing the anchor
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womon hillidde ratses heer arees, ambrucing the serine fandicque"
woman gradunllly falaiing from a poicend squat with lotr land near hor face"
wear jamps joyffilly londing gravefilly with ures raired!"
womto cheple forward, eniendinn her are with cherrfi expressoos
momon granefully mointaine a yage poue oe rosly surfice the oceon"
"A person ih block ribhod norllencck sior hoids green with chechhones"
"The camera part from kitchen, reveuling bathroowe thronghh deor"
temmmm trcnch cnal speakss animotally on
plune duckk"
Figure 8: More Visualization Results. each unit, the top row shows the results generated by original FLF2V model, and the bottom row shows the videos generated after applying AFB method. The added anchor frame are highlighted with red box.
frame, leading to more natural and coherent motion. Similarly, in the seventh case, the original video exhibits an unnatural phenomenon where water flows from the faucet and stops abruptly without external interaction-ant artifact that is successfully resolved with our approach.
C COMPARISON OF DIFFERENT ANCHOR FRAME INSERTION STRATEGIES
In the Anchor Frame Guided Generation step, we conducted a qualitative ablation study comparing two anchor frame insertion strategies. For Wan 2.1 Wang al. (2025a), the first approach averages the latent representation of the anchor frame, x with the noise latent, 2 at each denoising step, where f indicates the anchor frame position. In contrast, the second approach follows the original Wan 2.1 setup, applying no additional averaging operation. The comparison results are presented in Fig. 9. The observations indicate that the second approach outperforms the first, the first method leads to severe grayish blurring in the intermediate frames and fails to produce plausible video sequences. Therefore, the second approach is adopted the final method.
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couple walks along the sondly path toward the ocsan."
"The conters reveoly dining area while keeping the statrcase and living view.
Figure 9: Qualitative comparison of anchor frame insertion strategies. In each unit, the top row illustrates the anchor-frame frame averaging approach, and the bottom row shows the direct conditioning approach without averaging. The added anchor frames are highlighted with a red box.
Ground Truth
Wan 2.1
Wan 2.1 + AFB
Hunyuan
Hunyuan AFB
Figure 10: Complex scenarios. Existing 12V models struggle with complex scenarios, such as those featuring non-rigid motion, extreme viewpoint changes, occlusions. Although method yields observable improvements, does not fully resolve inherent issues like joint distortion and motion blur, which remain challenging.
D MORE DISCUSSION
D.1 CHALLENGING TASKS
Our method limited by the capabilities of the baseline 12V model. For challenging tasks invovling non-rigid motion, severe occlusions and Drastic viewpoint shifts, is difficult for existing I2V mod- els to accurately predict object trajectories and human body movements. As illustrated in the Fig. 10, while the insertion of our anchor frame yields some improvement over the baseline model, the mo- tion sequence remains unnatural when compared the ground truth. Artifacts such as physically implausible distortions and ghosting effects still present. However, believe that with the on- going advancements foundation models, combined with our method, these challenging issues will be gradually resolved in the near future.
D.2 RESEARCH ON PHYSICAL CAUSALITY
Our AFB method also demonstrates effectiveness in scenarios with strong physical causality (e.g., an orange falling into water, glass shattering, water droplet ripples, smoke dissipation, etc.). This is because the efficacy of AFB does not rely perfectly coherent reversed video- reverse gener- ation process merely serves as a means to obtain high-quality anchor frames that are highly relevant to the first and last frames. As long and maintains strong relevance to the first and last frames, AFB can function effectively, and this can be achieved through the reverse process.
obtain anchor frame that conforms to physical laws
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"An orange falls cup, spiashing draplets around."
"A glase gublet folls and shatters, spraying shards and liquid."
"A falls falls into watter creating ripples."
"Smoke rises, dissipates, leaving a faint trace.
Figure 11: Application examples of our method in scenarios with strong physical causality. Row 1: Video generated by reversing the first and last frames, where red boxes indicate the anchor frames selected during the reversal process; Row 2: Results using our method (AFB) with forward generation between the first and last frames, where red boxes represent inserted anchor frames.
Experimental results indicate that video generation based solely on first and last frames often pro- duces poor results the last frame, leading continuity breaks. Therefore, after mirroring the sequence, we select frames from the early segment of the reversed vidco as anchor frames. As shown in Figure 11, although the full reversed video exhibits physical distortions in its later part, the initial segment remains highly faithful. This suggests that even in extreme scenarios, the early stage of reverse generation still serves as highly reliable semantic source, enabling AFB to extract anchor frames from this interval and guide coherent forward video generation.
D.3 ANALYSIS ASYMMETRIC MOTION
"A sports accelerates and then cousts on a wide roud.""
Figure 12: Cases of Asymmetric Motion. Row depicts the results generated using only the first and last frames; Row shows the output from reversing the first and last frames, with the selected anchor frame highlighted by red box; Row 3 presents the generation after inserting the anchor frame, where the inserted anchor frame is marked with red box.
To investigate the impact of asymmetric motion on our method, we test the "car accelerating and then coasting" example. As illustrated in Figure 12, the decay of semantic constraints manifests similarly in both forward and reverse generations, regardless of whether the car fast or slow. Specifically, both directions exhibit motion discontinuities, such the car failing initiate acceleration naturally.
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In contrast, after applying AFB, the anchor frame effectively bridges this semantic gap, rendering the acceleration process significantly smoother and physically coherent.
D.4 ANALYSIS OF COMPLEXITY DIFFERENCE BETWEEN FIRST AND LAST FRAMES
practices parkour by ticking off wall. grabiinn bai. and srilngnng logp."
womsn walks stone corridr and soves
tarn before a
Gothic bailding.
"A blomson got capnurer mumont.
Figure 13: Analysis of Complexity Difference Between First and Last Frames. indicates the inserted anchor frame.
The red box
We conduct evaluations on three specific scenarios representing different challenges (Figure 13). Our findings follows: Complex Motion (e.g., Parkour): Despite the highly dynamic nature of the motion, our method successfully identifies a valid anchor frame, generating coherent high-speed actions. High Complexity Variance with Continuity (e-g., Character Scene Transition): In cases where the starting and ending backgrounds differ significantly but the subject remains consistent (e.g., the same character walking from corridor to the outdoors), AFB still functions effectively. The semantic continuity of the subject allows the anchor frame to bridge the complexity gap. High Complexity Variance without Continuity (e.g.. Flower - Girl): In extreme cases where the first and last frames are semantically unrelated, ,the generated result resembles disjointed, slideshow- like transition rather than a coherent video. However, we wish clarify that the primary objective of AFB is to preserve physical and semantic continuity; therefore, semantically unrelated boundary frames fall outside the scope of our current method. While AFB not designed to resolve this semantic disjointedness, addressing such non-continuous constraints remains interesting direction for future work.
E VALIDATION OF CONTINUITY BREAKPOINTS BETWEEN FORWARD AND REVERSE GENERATION
To validate the robustness of the hypothesis that continuity breakpoints occur at consistent posi- tions in both forward and reverse generation, we conduct large-scale quantitative experiments a complete dataset encompassing diverse motion types (e.g., asymmetric motion, severe deforma- tion). We computed frame-wise average LPIPS curves for all 436 videos during forward generation (lo + IN-1) and reverse generation (1N-1 + lo). Statistical result (Figure 14) reveals that: the quality collapse point (LPIPS peak) forward generation occurs at frame 56, while in reverse gen- eration it appears at frame 55, with merely a --rrame absolute positional deviation.
This provides strong statistical evidence that, regardless of video content, generation direction. or motion complexity, quality collapse points tend to occur at statistically consistent relative positions. This symmetry is not coincidental but fundamentally driven by "information decay" a systematic issue dominated by model architecture and error accumulation, largely independent of generation direction.
Specifically, most FLF2V models are adapted from 12V architectures. Their inherent characteristic that the semantic constraints from the first and last frames gradually weaken over temporal pro- gression (as shown in Figure (a)). During forward generation, frames move farther from the first frame, diminishing semantic constraints allow minor visual artifacts to remain uncorrected. These subtle deviations propagate, accumulate, and amplify camme-by-frame (Error Accumulation), even- tually causing the generated content to diverge from plausible trajectories and triggering continuity breaks at specific "semantic vacuum zones" (as shown in Fig. analysis).
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0.5
Generation Reverse Generation
0.4
LAPS
0.1
10
20
40
50
70
B0
Frome
Figure 14: Average LPIPS between adjacent frames in forward and reverse generation. Blue circles indicate positions where LPIPS exhibits extremum values, representing continuity breaks.
Although reverse generation alters the direction, the mechanisms of "constraint decay" and "er- ror accumulation" persist, leading to continuity breaks at positions similar to those in the forward process. AFB strategically leverages reverse generation locate the most severe breakpoints and inserts high-quality anchor frames at corresponding positions in the forward process, thereby miti- gating information decay at these critical locations.
F ADDITIONAL BASELINE COMPARISON
FCVG
MoG
Wan 2.1 + AFB
Figure 15: Qualitative evaluation. Red box highlights the defective portions in generated videos.
Table 5: Quantitative evaluation. The best results are bolded.
Baseline Models
LPIPS
FVD
SSIM f
PSNR
FCVG
0.19
438.83
0.86
33.62
MoG
0.26
421.53
0.69
18.41
Wan2.1 +AFB (Ours)
0.16
375.12
0.97
35.41
In Tab. 1, we compare against recent baselines under our default setting of generating 5-second videos. As FCVG Zhu al. (2025) and MoG Zhang et al. (2025) are typically evaluated on shorter clips (approximately 1-2 seconds), we include their comparisons here in the supplementary mate- rial. Qualitative results are shown in Figure 15, and quantitative results are presented Table 5. FCVG and MoG typically generate short clips (approx. 1-2 seconds). In contrast, AFB generates longer, complex videos (approx. seconds). As shown in Table 5, AFB significantly outperforms both FCVG and MoG. Specifically, AFB achieves the lowest FVD (2) and the highest PSNR
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(35.41), demonstrating superior temporal coherence and visual fidelity. As detailed in Figure 15, Both FCVG and MoG exhibit noticeable ghosting artifacts on static objects, such as the cabinet handles (highlighted in red boxes). In contrast, AFB leverages semantic anchors to generate clean, artifact-free frames with better logical coherence.
G ANALYSIS OF LONG VIDEOS WITH MULTIPLE ANCHOR FRAMES
"A girl and boy meet and talk the rooftop. He gives her hird feed, and as she offers pigeon lands on her hands eat."
Figure 16: Long videos with multiple anchor frames. Row shows the generation using only the first and last frames. Row presents the result with one anchor frame inserted. Row displays the output with two anchor frames added. Row 4 illustrates the generation with three anchor frames incorporated. All inserted anchor frames are highlighted by red boxes.
For further analysis, we employ Jimeng AI to generate 10-second long videos. The results (Fig- 16) demonstrate that for long-video generation, multi-anchor strategy (inserting or 3 frames) yields superior performance compared to the single-anchor approach. As the number of anchors in- creases, the temporal coherence of the intermediate frames also improves. For future work on longer videos and more complex scenarios, we plan to extend AFB by exploring adaptive multi-anchor in- sertion methods.
H USER STUDY
We design a questionnaire and distribute it to different participants. In the questionnaire, compare the results of Wan 2.1 and Wan 2.1 + AFB three aspects: video consistency, text alignment and generated video quality. We also evaluated how the number of anchor frames, K, affects generation quality, assessing our method across E (1, 2,3.3.. Further details of the questionnaire are presented below. A total of 52 valid responses collected and analyzed, with the statistical results shown in 17..17. It can be observed that our method is more favored by users compared to using Wan 2. alone. Additionally, among the different numbers of anchor frames tested, users clearly prefer the configuration with single anchor frame (K = 1).
Our Google Form survey consists of 4 sections, with a total of 33 questions. In the first three sections, we compare our AFB with Wan2.1 Wang et al. (2025a). In the final section, we compare videos generated with different frame insertion positions in AFB, Specific information and screenshots of the questionnaire are as follows:
1. Consistency in video generation. Users are asked to evaluate the temporal consistency of the generated videos with the given first and last frames. For instance, when provided with two images of a girl raising her hands alongside text description, users are instructed to select the video demonstrating bigher consistency. See Fig. 18 for details.
2. Text Alignment Video Generation. Users assess whether the video content accurately reflect the provided text descriptions. For instance, given text prompt describing smooth
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Wan 2.1
Wan 2.1 AFB(k=I)
Wan 2.1 AFB((=2)
Wan 2.1 + +FF((k-3)
100
40
80
Score (%)
60
Score (%)
30
20
20
Consistency
Text Aligument
Video Quality
Wan
AFB(K-I)
AFB(-2)
AFB(-33
Figure 17: User Study. The left figure shows user evaluations of Wan 2.1 Wang et (2025a) and Wan 2.1 AFB in terms of video consistency, text alignment and generated video quality. The right figure shows user evaluations on how number of anchor frames impacts video generation quality.
camera transition from kitchen bathroom along with first and last frames, users instructed to select the video that best matched the text prompt. See Fig. 19.
3. Visual Quality of Video Generation. Users select the video that exhibit the highest overall visual quality. For example, given a prompt such "A woman gracefully maintains yoga pose on a rocky surface by the ocean." along with first and last frames, users choose the video displaying superior visual fidelity, such as realistic water ripples or detailed rock textures. See Fig. 20.
4. Impact of Multiple Anchor Frames. Users compare videos generated using K anchor frames (K 1,2,3) 3) alongside the original Wan 2.1 FLF2V output. They instructed to vote for the video with the best overall quality in each case. The interface for this task is shown in Fig. 21.
In the user study, participants evaluate nine groups of videos in the first three sections and six in the final section. For each group, they select the video they consider to be of the highest quality. Each selection counts as point. We collect a total of 52 valid responses, and the aggregated results visualized Fig. 17.
21

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Published as a conference paper at ICLR 2026
Consistency in video generation
this section, you will be asked select the video that you believe best aligns with the given first and last frames and has good continuity. Please focus on the coherence of the video and how well matches the information conveyed by first-last frames. Note that video quality is not factor this evaluation.
The conditions for the generated video are provided, including the first-last frames as well as text descriptions. You only need select the one video you believe is the best; there is need for multiple selections ranking.
Which video has the best consistency?
first frame
last frame
"A woman standing on a hillside with her back to the camera gradually raises her arms above her head, embracing the serene landscape of trees and rolling hills under soft sky."
a
Figure 18: The instruction and example for user study.
22

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Published as a conference paper at ICLR 2026
Text alignment video generation
In this section, you will be asked to select the video that you believe best aligns with the given text descriptions. Please also take the quality the video or frames into consideration. For instance, videos with distorted limbs or deformed movements will considered less favorable.
The conditions for the generated video are provided, including the first-last frames as well text descriptions.
You only need to select the one video you believe is the best; there is need for multiple selections or ranking.
Which video has the best alignment with the given text descirption?
first frame
last frame
"The camera smoothly transitions from a wide view of cozy kitchen and bathroom area to closer, more focused shot of the same space, gradually removing the sink and countertop from the frame while maintaining the visibility of the stove, microwave, and open doorway leading to the bathroom."
Figure 19: The instruction and example for user study.
23

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Published as a conference paper at ICLR 2026
Quality in video generation
In this section, you will be asked to select the videos with the best visual quality. The conditions for the generated video are provided, including the first-last frames as well as text descriptions. You only need to select one video you believe is the best; there is no need for multiple selections ranking.
Which video has the best visual quality?
first frame
last frame
"A woman gracefully maintains a yoga pose on a rocky surface by the ocean,
transitioning smoothly from stance to another while the waves and coastal town remain serene backdrop."
O b
Figure 20: The instruction and example for user study.
24

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Published as a conference paper at ICLR 2026
Evaluation of Video Generation Methods
In this section, you will be asked to evaluate videos that differ due to slight variations in generation methods. Please select the video you prefer based on overall quality and coherence.
The conditions for the generated video are provided, including the first-last frames as well as text descriptions.
You only need to select the one video you believe is the best; there no need for multiple selections ranking.
Which video you believe is the best one?
first frame
last frame
"A woman gracefully transitions from standing pose with arms outstretched to a dynamic handstand on a grassy park area, while another person observes and the serene backdrop of palm trees and the ocean remains constant."
Figure 21: The instruction and example for user study.
25


Reviews:
[{"review_id": "NuNaC3Hmra", "reviewer": "Reviewer_QCjc", "summary": "The paper presents Anchor Frame Bridging (AFB), which is an innovative method to improve first-last frame video generation (FLF2V). The approach addresses a major issue: ensuring that all the frames between the first and last are consistent both in terms of their semantic content and the timing of events. The key idea is to insert a high-quality \"anchor frame\" that acts like a bridge in between the start and end frames. This helps reduce distortion and keeps the motion smooth. The authors show through their experiments that their method improves the quality and consistency of videos better than existing advanced models on their built datasets.", "questions": "The same to weaknesses.\n1. Physical Causality: Could the authors show examples of scenarios with definite, irreversible physical laws like shattering glass, a splash, or smoke dissipating to better explain where their method works best?\n2. Symmetrical Nature: Could there be an analysis or study to back up the symmetrical failure point assumption? Specifically, does this idea stand in different types of motion or when the complexity of the first and last frames vary greatly?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "+ The reverse generation process that selects the anchor frame is a smart way to tackle the problem of losing information as frames are generated.\n+ This method significantly boosts video quality and consistency compared to advanced baseline models, backed by various metrics and thorough evaluations, including user feedback.\n+ Since it's a plug-and-play module, it can be added to existing I2V diffusion models without needing any retraining, making it a very practical and broadly applicable tool.", "weaknesses": "+ Physical Causality Issues: The method generates frames in reverse order (last to first), but the model was trained with forward-time rules. This may create inconsistencies in scenarios with strong physical dynamics. For instance, reversing the video of ripples created by a stone drop doesn’t correctly model the physical event of ripples contracting to eject a stone. Although the authors’ datasets might not show this issue, it limits the method's use in situations with clear, irreversible physical laws.\n+ Assumption of Symmetrical Nature: The method relies on the idea that the weakest frame in reverse generation lines up symmetrically with the weakest frame in forward generation. This assumption is crucial for the anchor frame placement but is not clearly proven in the paper. It’s possible that complexities in the start and end frames could disrupt this symmetrical failure point.\n+ Computational Overhead: The method involves generating frames in two passes, which doubles the time for inference. The authors mention this in Section D.2, but say that this extra time is \"acceptable\" is arguable. Especially for models like CausVid, which need to operate with low latency and start playback before the video is fully ready. The requirement for a complete reverse-pass generation could undermine these models' real-time capabilities.", "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": "eL232v7wFF", "reviewer": "Reviewer_36Wi", "summary": "The paper proposes Anchor Frame Bridging (AFB), a training-free, plug-and-play method to address semantic degradation and temporal inconsistency in first-last frame video generation (FLF2V). AFB consists of two core modules: (1) adaptive anchor frame selection, which uses reverse generation (swapped first/last frames + Qwen-generated prompts) and LPIPS-based quality scoring to identify high-semantic-consistency anchors; (2) anchor-guided generation, which injects anchors into the video sequence to propagate boundary semantics. The authors construct a 436-pair FLF2V dataset, validate AFB on Wan2.1 and Hunyuan Video models, and demonstrate improvements (16.58% FVD, 10.21% PSNR on Wan2.1-I2V) via quantitative metrics, MLLM evaluations (GPT-4o, Gemini), user studies, and qualitative comparisons.", "questions": "Does the authors have plans to optimize the reverse pass—such as reducing denoising steps for anchor candidate generation or reusing latent features from the forward process—to lower latency, while ensuring the selected anchor frames still maintain high semantic consistency with the first/last frames?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "1. Practical, training-free design: AFB avoids the high computational cost of retraining large video models, acting as a plug-and-play enhancement—critical for real-world FLF2V applications (e.g., video editing) where retraining is infeasible.\n\n2. Rigorous validation: Results are supported by multi-faceted evaluation: standard metrics (FVD, PSNR, LPIPS), MLLM-based semantic assessment, user studies (52 participants), and ablations (anchor count, diffusion timesteps, prompt quality)—ensuring robustness.\n\n3. Targeted problem diagnosis: The paper clearly links temporal inconsistency to sparse inter-frame attention (Fig.1a) and semantic drift, with visualizations (Fig.6,7) that directly demonstrate AFB’s ability to bridge boundary-to-intermediate semantics.", "weaknesses": "1. Significant computational overhead: AFB doubles inference time (e.g., Wan2.1: 20→41 min; Table 3) due to the reverse pass for anchor candidates. The paper provides no roadmap for optimization (e.g., truncated denoising, lightweight anchor generation) to mitigate this..\n\n2. Limited edge-case improvement: AFB still struggles with extreme scenarios (non-rigid motion, severe occlusions; Fig.11) and relies on better base I2V models to resolve these—there is no exploration of extending AFB (e.g., dynamic anchor counts, motion-aware selection) to proactively address these failures.\n\n3. Missing references and comparisons for first-last frame consistency works: The paper’s related work (Sec. 2) and experiments (Sec. 4) fail to reference or compare with [1] and [2]—works that presumably address the same first-last frame consistency problem. Without detailing how AFB’s technical approach (e.g., reverse generation, anchor selection) differs from [1]/[2] or how its performance (e.g., FVD, visual coherence) stacks against them, the paper’s positioning as a competitive solution for FLF2V is weakened.\n\n4. Limited visual quality in supplementary materials: The supplementary material presents generated videos with limited quality, and the paper provides no direct comparison showing AFB outperforms [1] or [2] in visual fidelity. This lack of side-by-side visual evidence—critical for assessing FLF2V performance—makes it hard to verify AFB’s claimed advantages over these existing methods.\n\n[1] Zhu T, Ren D, Wang Q, et al. Generative inbetweening through frame-wise conditions-driven video generation[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 27968-27978.\n\n[2] Zhang G, Zhu Y, Cui Y, et al. Motion-aware generative frame interpolation[J]. arXiv preprint arXiv:2501.03699, 2025.", "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": "fDzBc6SmH5", "reviewer": "Reviewer_zLy5", "summary": "This paper addresses semantic degradation and temporal inconsistency in first-last frame video generation (FLF2V), where models must generate a video given only the start and end frames. The authors identify \"information attenuation\" as a key issue, where semantic guidance from the boundary frames weakens towards the middle of the sequence. To solve this, the paper introduces Anchor Frame Bridging (AFB), a novel, plug-and-play, and training-free method. AFB operates in two stages: The method first performs a full *reverse* generation pass (from last frame to first frame) to create a \"candidate set\" of frames. It identifies the frame with the maximum temporal incoherence (peak LPIPS) in this reversed sequence at a normalized position $\\alpha$. It then selects an anchor frame from the *mirrored* position ($1-\\alpha$) of this candidate set. The final video is synthesized by conditioning the base I2V model on the first frame, the last frame, and this new anchor frame at its designated position $\\alpha$, using an indicator mask to guide the diffusion process. The authors created a new dataset of 436 video pairs and applied AFB to two base models (Wan2.1, Hunyuan Video). Results show quantitative improvements (e.g., 16.58% in FVD on Wan2.1-I2V) and improved qualitative coherence in a user study.", "questions": "1. The doubled inference time is a major drawback. Have the authors explored cheaper approximations for anchor selection? For instance, Figure 5b shows LPIPS is unstable in early timesteps. Could a different, more stable metric (e.g., CLIP feature distance) reliably estimate the failure point $\\alpha$ at a much earlier, cheaper timestep (e.g., $t=10$ or $t=20$) instead of requiring a near-full reverse pass ($t=45$)?\n2. The finding that $k>1$ anchor frames degrades performance is counter-intuitive and the most significant concern for scalability. Does this imply a fundamental limitation in the \"anchor frame-guided generation\" step, which cannot resolve conflicting semantic guidance from multiple anchors? How do you envision this method scaling to longer videos (e.g., 300 frames) where a single anchor is clearly insufficient?\n3. $\\alpha \\rightarrow 1-\\alpha$ \"mirror\" heuristic is clever. But what are its failure modes? Have you analyzed cases where the motion is asymmetric (e.g., \"object appears\" vs. \"object disappears\")? In such cases, the point of max LPIPS in the reverse pass might be semantically unrelated to the *ideal* anchor location for the forward pass.\n4. The failure case analysis in Appendix D.1 is appreciated and shows failures inherited from the base models. Have you identified any failure cases *introduced* by AFB itself? That is, an example where the base model produces a coherent video, but the insertion of the selected anchor frame (which comes from a different generation pass) *causes* a new temporal break, artifact, or semantic mismatch?\n5. Related to Weakness #5, is it possible that the $k=1$ optimality is an artifact of your small dataset? How would you expect this to hold on a more diverse, large-scale benchmark with more complex, multi-stage motions?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The core idea of AFB is novel and clever. Using a reversed-generation pass to identify a point of failure ($\\alpha$) and then applying a \"mirror position\" heuristic ($1-\\alpha$) to select a high-quality anchor frame is an elegant, non-trivial solution to the problem of *what* to use as an anchor and *where* to place it.\n- The method is training-free and plug-and-play. This is a significant practical advantage, allowing it to enhance existing I2V models for the FLF2V task without any costly fine-tuning.\n- The paper is very well-written and easy to follow. Figure 1 and 2 provide an excellent, intuitive visualization of both the \"information attenuation\" problem and the proposed two-stage solution.\n- The authors validate their method on two different base models and use a comprehensive set of metrics (FVD, LPIPS, PSNR/SSIM, MLLM scores, and a user study) to build a robust case for the method's effectiveness. The ablation studies (e.g., on anchor frame count in Fig. 5a) are valuable and clearly justify the final design.", "weaknesses": "1. The most significant weakness is the computational overhead. The \"Adaptive Anchor Frame Selection\" stage requires a full, additional reverse generation pass (e.g., 50 diffusion steps) just to find the anchor frame. As shown in Table 3, this effectively doubles the inference time (e.g., from 20 to 41 minutes for Wan2.1). This high cost severely limits the method's practical utility.\n2. The ablation study (Fig. 5a and user study in Fig. 12) reveals that using a single anchor frame ($k=1$) is optimal, and $k>1$ degrades performance due to \"conflicting guidance.\" This is a major limitation. It strongly suggests the method is brittle and cannot scale to longer, more complex videos with multiple distinct actions or scene changes, which would naturally require multiple anchor points to maintain coherence.\n3. The entire anchor selection mechanism (finding max LPIPS in the reverse pass and mirroring the position $\\alpha \\rightarrow 1-\\alpha$) is a clever heuristic, but it is not a learned or guaranteed-optimal strategy. It relies on an implicit assumption that the motion and degradation are somewhat symmetrical, which may not hold for many real-world scenarios (e.g., a video of a car accelerating and then coasting).\n4. The paper positions AFB as a \"plug-and-play\" method. However, the experimental comparison in Table 1 is primarily against the base models themselves (Wan2.1-I2V, Hunyuan-I2V) or fully-trained methods (Wan2.1-FLF2V). The comparison lacks other relevant *training-free* or *guidance-based* video interpolation or editing methods that could also be considered \"plug-and-play.\" This makes it difficult to assess AFB's performance relative to its true peers.\n5. While the creation of a new dataset is commendable, the size (N=436) is very small for a video generation benchmark. This limited diversity makes it difficult to assess generalizability and raises concerns that the method's findings (especially the $k=1$ optimality) might be an artifact of the specific data distribution (e.g., short, single-action clips).", "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": "OcN6Z49TGL", "reviewer": "Reviewer_hmuv", "summary": "This paper investigates the challenge of semantic degradation and temporal inconsistency in first-last frame video generation using diffusion models. The authors propose the Anchor Frame Bridging (AFB) method, which automatically selects and inserts an adaptive anchor frame at points of maximal semantic discontinuity in the intermediate video. Their approach consists of an adaptive selection mechanism using LPIPS-guided frame evaluation and a guided generation process augmenting the standard pipeline with anchor frames. The method is designed as a plug-and-play module, requiring no additional training and is validated on newly curated benchmarks and multiple image-to-video diffusion backbones.", "questions": "- Could the authors clarify if any formal guarantees or theoretical results support the observed empirical optimality of the mirrored LPIPS-breakpoint approach over alternative anchor selection strategies or more adaptive criterion? What failure cases or counterexamples have you observed?\n\n- Can you elaborate on the impact and selection process for the denoising timestep at which LPIPS is computed, the window width for local averaging, and any anchor selection heuristics?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- **Clear Targeted Problem**: The paper identifies and clearly explains a critical shortcoming in current first/last-frame-to-video models—semantic drift and abrupt transitions in intermediate frames (see intro, Fig. 1).\n- **Methodological Innovation**: AFB uses a reverse-generation heuristic and LPIPS-based metric to localize semantic discontinuities and insert an anchor frame, effectively bridging boundary semantics throughout the sequence (Method Sec. 3, Eqns p4–p5, Fig. 2).\n- **Empirical Rigor**: The authors provide both qualitative and quantitative benchmarks, comparing AFB-augmented models against strong baselines (including Wan2.1, Hunyuan Video, ViBiDSampler, Generative Inbetweening) on a purpose-built test set.\n- **Comprehensive Ablation**: Multiple dimensions (number/position of anchors, prompt variants, effect of diffusion steps) are dissected with quantitative evidence (Tables 1, 2, Fig. 5a).", "weaknesses": "- **Limited Theoretical Insight into Anchor Utility**: \n\nWhile the method for selecting anchor frames is well-motivated empirically, there is only an informal rationale for why mirroring the LPIPS-based breakpoint in a reverse diffusion pass is optimal. The mathematical treatment (Sec. 3.2, Eqns p5) relies on an empirical proxy rather than theoretical guarantees. This leaves open whether the anchor truly maximizes semantic continuity for all types of motion or scenes, or merely correlates empirically. A more thorough theoretical foundation is desirable to formally ground the anchor selection beyond heuristic evidence.\n\n- **Complexity in Hyperparameter Selection and Heuristic Choices**: \n\nThe anchor frame is chosen based on the peak of a smoothed local LPIPS curve at a fixed (empirically chosen) denoising timestep (Fig. 5b). But the paper provides limited discussion of sensitivity to these hyperparameters (e.g., LPIPS window size, selected denoising phase, anchor mirroring formula). There is a risk that such choices may not generalize, or that their necessity complicates adoption.\n\n- **Visual Quality Trade-offs in Extreme Cases**: \n\nWhile the reported objective and subjective gains are strong (Table 1), failure cases and remaining artifacts (Appendix D, Fig. 11) suggest that AFB does not fully resolve physical implausibility or distortions in highly dynamic settings. This is acknowledged, but could be further emphasized as a hard limitation.", "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)