EchoShot: Multi-Shot Portrait Video Generation
Authors: Jiahao Wang
Hualian Sheng
Sijia Cai
Weizhan Zhang
Caixia Yan
Yachuang Feng
Bing Deng
Jieping Ye
Venue: NeurIPS 2025 poster ()
Keywords: Portrait Video Generation
Multi-shot Video Modeling
Rotary Position Embedding
Portrait Video Dataset
Primary area: applications
TL;DR: a novel multi-shot video modeling paradigm via a new form of RoPE for portrait video generation, along with a large-scale portrait video dataset

Abstract: Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world applications urge multiple shots with identity consistency and flexible content controllability. In this work, we propose EchoShot, a native and scalable multi-shot framework for portrait customization built upon a foundation video diffusion model. To start with, we propose shot-aware position embedding mechanisms within the video diffusion transformer architecture to model inter-shot variations and establish intricate correspondence between multi-shot visual content and their textual descriptions. This simple yet effective design enables direct training on multi-shot video data without introducing additional computational overhead. To facilitate model training within multi-shot scenarios, we construct PortraitGala, a large-scale and high-fidelity human-centric video dataset featuring cross-shot identity consistency and fine-grained captions such as facial attributes, outfits, and dynamic motions. To further enhance applicability,  we extend EchoShot to perform reference image-based personalized multi-shot generation and long video synthesis with infinite shot counts. Extensive evaluations demonstrate that EchoShot achieves superior identity consistency as well as attribute-level controllability in multi-shot portrait video generation. Notably, the proposed framework demonstrates potential as a foundational paradigm for general multi-shot video modeling. Project page: https://johnneywang.github.io/EchoShot-webpage.

EchoShot: Multi-Shot Portrait Video Generation
Jiahao Wang'
Hualian Sheng* Yachuang Feng?
Sijia Cai
Weizhan Zhang
Caixia Yan
Bing Deng²
Jieping Ye²
School of Computer Science and Technology, MOEKLINNS, Xi'an Jiaotong University Alibaba Cloud Computing
jiahaowangostu. xutu. edu. en stephen. caj caj@alin Dealibaba-inne zhangwzh@x tu. edu. cn
Abstract
Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world applications urge multiple shots with identity consistency and flexible content controllability. In this work, we propose EchoShot, native and scalable multi-shot framework for portrait customization built upon foundation video diffusion model. To start with, propose shot-aware position embedding mechanisms within the video diffusion transformer architecture to model inter-shot variations and establish intricate correspondence between multi-shot visual content and their textual de- scriptions. This simple yet effective design enables direct training on multi-shot video data without introducing additional computational overhead. To facilitate model training within multi-shot scenarios, we construct PortraitGala, large-scale and high-fidelity human-centric video dataset featuring cross-shot identity con- sistency and fine-grained captions such as facial attributes, outfits, and dynamic motions. To further enhance applicability, we extend EchoShot to perform refer- ence image-based personalized multi-shot generation and long video synthesis with infinite shot counts. Extensive evaluations demonstrate that EchoShot achieves superior identity consistency as well attribute-level controllability in multi-sho portrait video generation. Notably, the proposed framework demonstrates potential foundational paradigm for general multi-shot video modeling. Project page: https: ohnneywwnn github. io/ Bete//ehhhhtteeeeeeeeeeeee webpage.
Introduction
Recent advancements in Diffusion Transformer-basee (DiT) model [34] have catalyzed transformative progress ext-to-video (T2V) generation, enabling the synthesis of visually realistic single-shot videas [10, 6, 4, 3, 43]. In real-world content creation workflows, users frequently demand multi- shot video generation with persistent subject consistency, particularly human-centric applications such narrative storytelling, virtual try-on with background variations, and appearance attribute editing. However, prevailing T2V models exhibit limitations in achieving identity consistency when generating multiple human-centric videos, attributable pre-training on independently fragmented single-shot data and a lack of effective cross-shot post-training strategies.
To tackle this challenge, we present a novel multi-shot portrait video generation (MT2V) task. As straightforward solutions, existing studies can be organized perform this task through two
Project Lead
Corresponding Author
This work was completed during the internship at Alibaba Cloud Computing.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Consistent:
ICAMERAI [ATTRIDUTE] [[AIRSTYLL] JAPPAREL/ [EXPRESSION)
1: [BEHAVIOR] stond shill and talk [BACKGROUND) blur lighting building
Shot [BEHAVIOR)
3:
[BERAVIOR]
sit by the window, holding cup of coffee. gently tasting
take a leisunely walk, gaze into the distance (BACKGBOUND) vast beach, sengulle flying
[BACKGROUND)
in cozy and romantic cofé
Consistent:
[ATTRIBUTE] [HAIRSTVIE [APPAREL]
Shot 1:
Shot 2:
9
Shot 3:
(CAMURA) arc-shot, clese-shat
[CAMERA]
zoom [CAMERA)
zoom shot middle-shot
middle-shot
(DEILNTOR))
hand reised, finger pointing forward
smile, blink
hold a phone
fingers swiping
[BACKGROUND
[BACKGROUND]]
[BACKGROUND] Bedroom. pink photos, posters
shop counter various goods
froes and
distrance
Consktent:
[CAMERA] [NTTRIBUTE] [HAIRSTYLE]
1:
Shot
[APPARFL]] a dark blue blazer paired with light-colored shirt [EXPRSSSINN] smile, with soft eyes conveying friendlness and cose (EEHAVOOR) raise her left hand then right hand to adjust her hair [BACKGROUND] white walls with black-ard-white photographs
[APPAREL a white base sleepweer outfit with pink vertical stripes
EEXPRESSINN furrowed brows, downtrreed lips, sodness and sorrow
[BEHAVIOR] | loan against headboard then cover chest with right hand
[BACKGRDD IDD a dark wooden headboard, ned thet wallpaper
Consistent: ICAMERAI AATRRBLUTEJHHARSSTTT
Consistent:
[CAMERAI |ATTRIBLTEEJJAAPPRRRRE
REPPRSSIINN] [BEHAVIOR] CRCRUGDDND)
JEXPRESSION| [GEHAVIOR] (BACKGROUND)
Shot 1:
Shot 2:
Shot 3:
Shof 1: [HARSTYYL]] light brown, curly.
Shof
X
Shot 3:
(APPAREL]
[APPAREL]
(APPAREL)
[HIRSTYYE]]
HAIRSTYLE
layored vest
derim jocket,
sweater stor patterns
alicked-bbckk combed
mohawk,
striped
mediun-lenath
Figure 1:
Given multiple formatted prompts of the same character, EchoShot generates multi-shot portrait videos showing the same appearance with superior fine-grained controllability.
paradigms: (1) Iterative personalized generation. This approach achieves cross-shot consistency
through recurrent applications of personalized text-to-video (PT2V) models [7, 53, 4, 3] but faces challenges in terms of textual controllability and full-body consistency. To specify, the facial feature guidance mechanism in PT2V models often leads adversarial competition between identity preser- vation and prompt adherence, reducing the controllability in aspects such as hairstyle, expressions, and apparel. Moreover, these models, tailored for facial preservation, fail to maintain the whole body consistency across generated videos. (2) Keyframe-to-we to-ne-tt-vdeo synthesis. This approach first generates multiple keyframes using consistent text-to-image (CT2I) models [56, 42, 20, 44, 45] and transforms them into multiple shots utilizing sophisticated image-to-viden (I2V) models [4, 3, 43]. Although this decoupled strategy partially mitigates the multi-shot consistency challenge, fundamentally suffers from the bottleneck effect and highly depends on the performance of CT21 models. The flaws of keyframes, such as blur, distortion, and inconsistency. will severely degrade the quality of generated videos. Besides, the absence of explicit identity conditioning in I2V models leads to progressive degradation of facial consistency during temporal expansion, causing dynamic identity drift.
For these issues, we explore novel paradigm for native multi-shot consistent video generation in this work. Our motivation lies in the investigation of whether constructing a temporally coherent portrait dataset and implementing comain--peciffe post-training strategies can enable the DiT architecture to intrinsically model appearance-motiin consistency across multi-shot videos. To this end, we introduce EchoShot, advanced multi-shot portrait video generation framework built upon the state- of-the-art video DiT model Wan2.1-T2V [43], with our work contributing three core innovations: (1) We propose PortraitGala, a large-scale portrait dataset comprising 250k one-to-one single-shot video

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clips and 400k many one multi-shot video clips. Each video undergoes rigorous identity clustering via facial embeddings and is annotated with granular captions covering facial attributes, dynamic expressions, and scene conditions, ensuring strict consistency and fine-grained controllability in the final generated videos. (2) To enable efficient multi-shot post-training based on the pretrained T2V model, we integrate a new position embedding mechanism termed Temporal-Cut RoPE (TcRoPE) into the DiT-based visual token self-attention module to resolve challenges in variable shot counts, flexible shot durations, and inter-shot token discontinuities. In the video-text cross-attention module, a Cemporal-Aligee RoPE (TaRoPE) with a mismatch suppression mechanism is further introduced to establish strict alignment between text descriptions and their corresponding video contents within each shot. (3) Based on our trained EchoShot model, we further extend its capabilities by incorporating an external facial encoder for reference image- basee personalized multi-shot video generation (PMT2V), and designing a disentangled RefAttn mechanism for infinite shots video generation (InfT2V). These extensions highlight our model's 's flexibility and generalization in real-world content creation.
Our experiments demonstrate that EchoShot excels in generating multi-shot portrait videos with consistent cross-shot identity preservation, while supporting fine-grained attribute control and scene manipulation. Comprehensive evaluations confirm its superiority over existing methods in quantitative metrics and user studies, particularly in maintaining identity coherence under complex motions and content variations, all within a native multi-sho video generation architecture.
2 Related Work
Video diffusion models. The video generation field has witnessed remarkable progress over the past year, marked by Sora [10]'s pioneering adoption of scalable DiT architecture. Subsequently, both closed-source models like Kling [4]. MovieGen [35], Hailuo [3], Veo2 [6] and Gen-4 [2]. along
with open-source alternatives such Open-Sora-Plan [27], Cog [51], Hunyuan Video [24], StepVideo [31], and Wan [43], have achieved remarkable and rapid advancements in video generation quality. Key technical strides stem from architectural shifts from U-Net [36, 9] to DiT/MMDIT
[38, 16] to 3D full attention in video token interactions, model optimization from DDPM [36] to flow matching [15], advanced Video- VAE [51, 43] and enhanced text encoders [35, 24J. Despite advancements, existing methods are limited to single-shot videos, and extending pretrained T2V models for multi-shot scenarios poses a critical challenge.
[34, 15], the evolution from
Consistent generation. Maintaining subject consistency across scenes has broad real-world applica- tions, with early efforts focusing image consistency for story visualization and comics. Techniques include iterative use of personalized text-to-image models [26, 47, 18] for similarity preservation, or leveraging self-attention mechanisms [56, 42] and in-context capabilities [20] for multi-image align- ment. With advances in video generation, these approaches could be applied to keyframe generation, combined with 12V models for multi-shot video synthesis. For example, VideoStudio [30] integrates entity embeddings for appearance preservation. MovieDreamer [54] predicts coherent visual tokens that are then decoded into keyframes, and VGoT [55] employs adentity-preserre for cross-shot consistency. However, challenges persist in achieving high-fidelity dynamic identity consistency and controllability. Recent work explores fine-tuning T2V models for multi- and multi-shot generation. Hunyuan Video [24] supports two-shot generation through caption concatena- tion. TALC [8] enhances temporal alignment with improved text conditioning, MinT [48] introduces time-aware interactions and positional encoding, and LCT [17] extends single-shot models using scene-level attention, interleaved 3D embeddings, and asynchronous noise strategies. While LCT emphasizes cross-shot coherence via scripted entity descriptions, our method focuses on identity consistency and text-guided control in multi-shot portrait generation.
embeddings
Human video datasets. Diverse, large-scale human-centric video datasets are crucial for advanc- ing portrait video generation. While existing high-quality portrait datasets like CelebV-HQ [57], CelebV-Text [52], and VFHQ [49] primarily feature close-up heads or upper bodies, other action datasets such as UCF101 [39], ActivityNet [11], Kinetics700 [12] suffer from inadequate visual quality and inconsistent face visibility. Recently, OpenHuman Vid [25] introduces large-scale and visually realistic dataset sourced from films, TV series, and documentaries for single-shot video pretraining/fine-tun However, both specialized portrait datasets and general pre-training datasets [13, 46] currently only offer single-shot video clips, leaving a critical gap in high-quality multi-shot identity-come consisttt portrait video datasets.
3

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Shor
Norm Project Unpaachity & Spii
Shote
Shor
CrossAriession
10
TaRtPP
TaRoPE
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Time
Self-Attenton
db TURAPP vhouiization
TcRoPE
Sbow
Shot
Concat & Add noise Patch embedding
Shot 2
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Cayine
Lambon
VAE Encoder
VAE Encoder
VAE Encoder
VAE Encoder
kd
ke
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Shot 0
Shot 1
Shot
Shot
Figure 2: (a) The overall architecture of EchoShot, a multi-shot video generation paradigm, which features two intricate RoPE mechanisms. (b) TcRoPE, 3D-RoPE that rotates an extra angular rotation at every inter-shot boundary along the time dimension. (c) TaRoPE, ID-RoPE that differentiates between matching and non-matching shot-caption pairs. Note that the visualization displays only one rotational component, with others excluded for simplicity.
3 Method
Overview. To accomplish multi-shot portrait video generation, we first review current single-shot models in Sec. 3.1. As a foundation, we devise and train a multi-shot text-to-video (MT2V) modeling paradigm in Sec. 3.2. We further tailor the personalized and infinite extensions in Sec. 3.3. All the training is driven by a meticulously curated dataset in Sec. 3.4.
3.1 Preliminary: Single-shot T2V Generation
Given video segment æ during training, prevailing T2V models first encode into latent feature: (xx), where & is pretrained video encoder. The latent feature is then mixed with Gaussian noise c, becoming noisy sample 2,. The training is driven by denoising process of rectified flow (RF) formulation [29, 15]: f = E...r.xll(c =) - uz(=,, T, c)ll where r E [o, 1] is the denoising timestep, (.(-) is a denoising network and is the embeddings the textual description. A typical implementation of (-(-) is DiT, combining self-attention and cross-attention layers. In the self-attention layers, given 3DD-position-indeeed query key vector f t, .Rd 3D Rotary
Position Embedding (RoPE) [15] adds dimension-wise position information to it, forming J
Fu EDD-RoPEffth... h, w) = Re 6sa t.k,w
where R represents 3D rotation matrix determined by the positional index and set of base angles O.. Details are provided in the Appendix. Notably, RoPE is generally excluded from cross- attention layers. While single-shot T2V models can generate portrait videos, extending this capability to multi-shot T2V models while preserving consistent identity remains a significant challenge.
3.2 Modeling Multi-shot T2V Generation for Consistent Identity
This task focuses generating coherent multi-shot portrait videos that exhibit consistent identity across all shots while allowing flexible, user-defined control over both content and appearance. During training, each instance consists of multi-shot video paired with corresponding frame counts and textual descriptions for each shot: {æ,, f1.:
(ts; ps 1-=1 where S denotes the total number of shots. Here, n, represents the frame count for the s-th shot, and p. specifies its textual prompt. During inference,
users are empowered to define the desired frame counts and prompts for S' shots: {n., )="=1 enabling customized multi-shot video generation with consistent identity. This task presents three key challenges compared to single-shot video generation:

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Variable Shot Lengths: Each shot's duration (i.e., frame count) can be flexibly defined by the user, accommodating diverse temporal requirements without imposing rigid constraints. Identity Consistency Across Shots: The generated video must ensure that all shots depict the same individual identity, maintaining seamless visual coherence throughout the sequence.
Text-Driver Control of Each Shot: Both the background and foreground in each shot are controlled via textual prompts, enabling precise customization of scenes and characters.
Identifying the inter-shot boundary via TeRoPE. Starting with S training video segments, a video encoder is first utilized to separately encode them into compressed latent features 2s, as shown in Fig. 2(a). Then, the latent features are further projected into query, key, and value embeddings to prepare for the self-attention mechanism. To preserve relative positional and temporal information, in vanilla 3D-RoPE, query and key embeddings require being modulated by the indexes of time, height, and width. However, such temporal index modulation is based on the assumption of continuous video content, which is not suitable for multi-shot scenarios with discontinuous video content. For example, the final frame of the first shot should correlate strongly with its preceding frame and weakly with the subsequent frame (i.e., the initial frame of the second shot). To model such an intricate correlation, as shown in Fig. 2(b), we raise TcRoPE to modulate the query and key embeddings, which incorporates a rotary phase shift between every two shots. For the feature embedding aa ai,,,,, at time t, height h, width w, and shot s, the processed feature can be denoted as:
ft. TcRoPE( F.A,,,,. t, h, w, s) where j is the phase shift scale. Intuitively, TeRoPE delineates the inter-shot boundary along the whole timeline concisely via an extra angular rotation. This method paves the way for precise control over the number of shots and the duration of each shot.
".. S.J. j. h.h,,.) w),
(2)
After the self-attention process, single-shot T2V models indiscriminately fuse semantic and visual modalities without incorporating positional information. In multi-shot scenarios, a straightforward approach is to concatenate all shot-wise captions into single comprehensive caption. However, this method imposes limitations on the length of each shot caption, given that the total caption must remain within the input window size of the text encoder.
Associating the semantic and visual modality via TaRoPE. Therefore, it is necessary to establish a shot-wise intricate correspondence when processing multi-shot videos and their associated captions. Specifically, the query of a specific shot should fully interact with the key of its corresponding caption, while maintaining a limited interaction with the keys of other captions, because simply ignoring interactions with other captions would risk discarding potentially useful supplementary details that could enhance the representation of the shot. To achieve such delicate interactions, first input the captions separately into the text encoder to generate their respective embeddings. These embeddings are then concatenated to form the complete caption representation. Subsequently, as shown in Fig. 2(c), we propose TaRoPE to modulate the visual query embeddings and textual key embeddings. Given the i-th feature embedded in shot s, the processed feature can be expressed as:
fh. TJ s) = ID-ROPE( (f.,,, k),
where is a hyperparameter that controls the mismatch suppression scale. Following feature modulation, attention calculation is performed, and the attention score between the query of 8 -th shot and the key of 82-th shot is given by:
A = AeL.z 8(k(s1 - =2l),
(4)
where As ,s represents the standard vanilla attention, and 5(.) is monotonically decreasing function within specific input interval, with (()) = 1. Detailed derivation is provided in the Appendix. Consequently, the attention between the matched query and key (i.e., when 81 = 82) remains identical to that vanilla models. In contrast, the attention between unmatched pairs is suppressed, controlled by k. Finally, the processed features are passed through an FFN to generate the output.
Our proposed TeRoPE supports variable shot lengths and counts, while TaRoPE enables text-driven control of each shot. The multi-shot training paradigm inherently ensures identity consistency across all shots. Notably, the two proposed RoPE mechanisms introduce no additional parameters.
3.3 Towards Personalized and Infinite Multi-shot T2V Generation
PMT2V. The aforementioned method enables the model to generate video segments while preserving consistent identity. However, a key application involves extending the model's capability to generate
5

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1) During flne tunlng
23 During infervnce attempts
PMT2V pipelinee
(b) InfT2V pipeline
Figure 3: Two enhanced pipelines based on MT2V model. (a) PMT2V pipeline, with an integrated conditioner branch, generates multi-shot portrait videos of given face input. (b) InfT2V pipeline creates infinite shots of the same person across multiple generation attempts, enabled by RefAutn, which disentangles the first shot as constant reference.
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25,195
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shirl while eeattnn eyés gaze
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femate holding short natural
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ard Beatly blue buml
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Number of Shots
26
(a) Caption case
(b) Word clond of captions
(c) Distribution shot-mise
Figure 4: (a) A caption case of PortraitGala. Each clip is thoroughly captioned in the fine-grained format. (b) The word cloud reflects the comprehensiveness of the captions. (c) PortraitGala consists of 650,000 clips with 400,000 IDs, totaling video duration of 1,000 hours.
video segments corresponding to user-specified image. To achieve this goal, as illustrated in Fig. 3(a), we incorporate a conditioning branch to encode the face input using face encoder, which consists of AreFace [14] and CLIP-G [41]. The resulting face embeddings are concatenated with the semantic embeddings of each text prompt, modulated by TaRoPE, and subsequently fed into the cross-attention layers. The denoising network fine-tuned from the MT2V weights, guided by the standard RF loss.
InfT2V. In real-world applications, life-long video generation with an infinite number of shots for the same individual is required. A natural approach is to fix the first shot as reference across multiple generation attempts while varying the remaining shots. The inherent dynamics of the full-attention mechanism update the denoising path of each shot based on the fixed noise input from the first shot and the random noise inputs from remaining shots, resulting in variations in the first shot across different generation attempts. To address this issue, we disentangle the first shot using RefAttn, as illustrated in Fig. which computes two attention weight matrices:
(5) where [-] is concatenation operation. Here, Ai is employed update the first shot, while Ag is utilized to update the remaining shots. Integrating RefAttn, the model undergoes a quick fine-tuning learn this new pattern. During inference, maintain the noise, length, and prompt of the first shot unchanged across different generations, providing a fixed reference. As we change the prompts of the remaining shots, our method generates infinite shots while preserving the identity of the first.
A = Q1 x K1. A = [Q2.... [KI....]
3.4 PortraitGala: The First Multi-shot Portrait Video Dataset
Data processing. To support training, we meticulously construct high-quality and large-scale scale human portrait dataset through an incremental process: 1) collect a raw data pool from diverse sources, including movies, episodes, open-source dataset, etc.; 2) filter by aspect ratio, resolution, and other criteria; 3) slice into single-shot clips using PySceneDetect [5]: 4) detect human face [23] and retain single-perss person clips; 5) identity many-to-one clips showing the same person using in-house

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[ATTRIBUTE]
youth
support more than shois [HAISSTYLE] short, hair, slightly inward
Shot 3:
Shot
[APPAREL]
[APPARE1]
[APMARFF]
dark blue shirt with blue white T-shirt underneath [CAMERA) static, close-shot [UXPRESSION] poinful and sorrow AVTOR burst out (BACGRROUND)
white long-sleeve shirt, light skirt, convos bog
fine ine-striped blozer
[CAMERA]
[CAMERA]
(CAMERA
tilt-up, middla-shat
hand-hold, middle-shot
static, close-shod
[EXPRUSSION]
[EXPRESION]
[EXPA
first focus and colm. then little serious
happy, expectont friendly
[SEHAVIOR)
[BLILANIOR]
[BERLWIIOR
first sit of desk, Then stond and store ahead
open
emerging behind
hold phone speak
[BACKGROUND] office desk cluftered with files, with window
BACKGROUND)
[BACKGROUUD)]
pin pantrry water dispenser".
IC-LoR AARRAAWN Wan StoryDiffusin Wan Wan
E
inconsistent identity, not follow prompts
inconsistent identity, dynamic drift, not follow prompts
Hunyuan Video
inconsistent identity, follow prompts, shot prompt intermingling
EchoShot
naturally support numerous shots, consistent details, highly align with shot-wise prompts, fine-grained controllability
Figure 5: Illustration of EchoShot and baselines in the MT2V task. Key prompts marked blue.
Our method demonstrates superior appearance consistency and fine-grained controllability.
facial embedding extraction and clustering pipeline; 6) remove duplicate videos showing similar content; 7) attach attribute labels, text descriptions, and other necessary structured information based on Gemini 2.0 Flash [1].
Data format and statistics. We deconstruct the content of portrait videos into aspects and establish a unified caption format. To specify, as shown in Fig. 4(a), every clip is annotated with the format "Th is is [CAMERA] of [ATTRIBUTE] [HAIRSTYLE] [APPAREL] [EXPRESSION]] [BEHAV IORJ [BACKGROUND] [L [LIGHTING] " The word cloud in Fig. 4(b) reflects the comprehensiveness of our detailed captions, which endows our model with fine-grained controllability of the generated videos. As a result of the curation pipeline, we ultimately build up PortraitGala, which consists of 600k clips showing 400k identities, totaling a video duration of 1k hours. As depicted in Fig. 4(c), our dataset includes 247k single-shot IDs, 107k two-shot IDs, 25k three-shot IDs, and so which lays a solid foundation for our multi-shot training paradigm.
Experiments
Implementation details. EchoShot is implemented based on Wan- 1.3B [43]. Throughout the three tasks, we the resolution to 882x480 and use fixed 125 frames, which is equivalent to 7.8 seconds in the real world. We set the phase shift scale j to 4 and the mismatch suppression scale k to 6. The training dataset consists one-third one-to-one data and two-thirds many-to-one data, and the shot number S varies from 1 to 4. The length of each shot is randomly sampled. All the training is driven by the standard RF loss. The MT2V pretraining takes about 3,500 NVIDIA A100 GPU hours. The PMT2V model and InfT2V model are fine-tuned based on the MT2V weights.

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Consistent:
Consistent:
[[AIRSTYLE] short, groy hair
[HAIRSTYLE] light golden hair with black hairband
[APPAREL] wearing cardigan
Shot
Face input:
input:
[APPAREL wear a light blue shirt
[XXRESSION] serious and focused
[BACKGROUND) luxuriously decorated room
DELLAVIO) sit on sofe, hold white vintage telephone
[BEHAVIOR] raise head slightly and is spesionng
[BACKGROUND) a dark forest with mist
[EXPRESSION happy and joyful
Shot
Shot
[AWPREE]] wear o red shart-sleeved dress, with glosses
[EXPRESSION] re relaxed and cheerful
[XXRESSION] wornied uneasy
[RERAVIOR] racking chain
[REAAVOOR] stond still, gaze downward
[RACKGROUND]] sunlit porch, lush green gross behind
[BACKGROUND]] dark forest with
Shet 3:
Shot
JAPPAEEL] block dress
[EXPRESSION]
[EXPRESSION] extramely and poinful
[BEELAVIO)] nest park bench, eyes (BACKGROUND] park,
[NETLAVORR] tears reddened eyelids
[BACKGROUND] brown woll odorned with patterns
ConsislD
inconsistent clothes, visual quality
bias reference, follow prompts, visual quality
Kling
inconsistent clothes, inconsistent artistic style, doesn't follow
bias to reference, docse't follow prompts, inconsistent identity
EchoShot
consistent uppearance, vivid expressions, highly aligns with prompts, fine ineeggaieed controllabillit, good visual quality
Figure 6: Illustration of EchoShot and baselines in PMT2V task. Baselines exhibit severe bias, poor consistency, and limited controllability, while our method demonstrates superior overall quality.
Figure 7: Illustration EchoShot in InfT2V task. Our method succeeds generating infinite shots (10 here) showing the same identity.
Baselines. To evaluate the performance of EchoShot, we compare it with a variety of baselines. For MT2V, we include (1) keyframe-bar based pipelines, StoryDiffusion [56]+Wan-I2V [43] (SD+W) and IC-LoRA [2n]+Wan-I2V [43] (IC+W), (2) Hunyuan Video [24]. which supports two shots generation.
For PMT2V, we include (1) open-source ConsisID [53], (2) closed-source Kling [4]. All model
settings are kept at their default values.
Benchmark. To provide an objective assessment, we instruct LLM generate 100 sets of multi-shot prompts in the portrait caption format. The prompts are transformed into different versions to match the different text input lengths of baselines. We encompass metries from three different aspects: (I) Identity consistency. Following [53], we use FaceSim-Are [14] and FaceSim- -ur [21] to measure the facial consistency across different generated shots. (2) Prompt controllability. We instruct VLM to score the alignment between the video and the corresponding prompt in three dimensions: human appearance (App.), camera and human motions (Mot.), background and lighting (Bg.). (3) Visual quality. Since traditional metrics may not faithfully reflect human preferences, we follow [22] to instruct VL.M to score two dimensions. Statie quality (Sta.) covers clarity, color saturation, content layout, etc. Dynamic quality (Dyn.) covers smoothness, temporal consistency, reasonableness, etc.

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Identity consistency FaceSim-Cuu
Prompt controllability
Visual quality
Method
FaceSim-Are
App.
Mot.
Bg.
Sta.
Dyn.
StoryDiffusion+ Wan--i44 14B
68.65
65.29
83.33
72.50
69.53
63.20
84.68
IC-LoRA=Wan-14h
68.45
65.04
87.59
83.71
75.37
79.49
87.98
Hunyuan Video-1d (2 shots)
64.80
61.25
83.39
68.15
72.37
86.37
87.22
EchoShot- (ours)
73.74
69.43
95.84
88.86
94.72
88.91
87.85
Table I: Metrie results in MT2V task. Requiring only IB parameters, EchoShot tops the scoreboard compared to baselines. The best and second-best scores are denoted bold and underlined.
4.1 Qualitative Evaluation
MT2V. As shown in Fig. 5, limitations of baselines are observed: (1) The performance of the Keyframe-basee pipelines constrained by keyframe priors. To specify, SD+W fails follow the detailed prompts (c.g., blue blazer, ID badge), and the visual style appears unnatural. Both SD+W and IC+W exhibit suboptimal consistency across shots. (2) The keyframe-based pipelines exhibit progressive degradation of facial consistency during temporal expansion. For example, the crying person in shot #2 generated by IC+W shows obvious inconsistency with other shots. (3) Simply concatenating the multi-shot prompts, adopted by Hunyuan Video, causes the intermingling of prompts of different shots and inferior controllability. For instance, in the videos generated by Hunyuan Video, the window intended for shot #1 unexpectedly appears in shots #2. By contrast, benefiting from the multi-shot modeling, our EchoShot generates numerous shots with consistent details as well as fine-graines controllability, demonstrating superiority over the baselines. Notably, EchoShot is able to generate a coherent sequence of human actions, such as the "body emerging from behind the door" in shot #2, whereas keyframe-based approaches struggle to achieve.
PMT2V& Fig. 6 illustrates the comparison between our method and baselines in PMT2V task. Primarily trained on a one-to-one portrait dataset, both ConsisID and Kling exhibit severe bias to the reference image and fail follow the prompts (e.g., the hairstyle and expressions of Emma Watson). Besides, the repeated generation process lacks the ability to maintain consistency across different shots (e.g., the clothes of Morgan Freeman). By contrast, with many-to-one modeling and shot-aware mechanisms, our method achieves consistent appearance, vivid expressions, and precise controllability across shots. Additionally, we visualize the performance of our method in InfI2V task in Fig. 7. Thanks to the RefAttn mechanism, our method is able to generate infinite shots of freely customized prompts while maintaining the same identity of the character.
4.2 Quantitative Evaluation
Metric results. Tab. I shows the quantitative metrics of EchoShot and baselines in MT2V. For identity consistency, our method scores the highest 73.74% on FaceSim-Are and 69.43%
vs SwryDiffusion Wan IC-LRRA War HunymaViida
86.7
80
76.9
78.7
Winning rite (%)
66
68.9
68.5
on FaceSim- Cur, achieving strong cross-shot identity consistency. In terms of prompt con- trol, our method remarkably scores 95.84%
61.0
60
40
App., 88.86% Mot., and 94.72% Bg., surpassing the baselines by a large margin. Besides, our method scores 88.91 % Sta. and 87.85% Dyn., both top-tier. The results prove the overall ad- vantages and the effectiveness of our method.
20
Identity consistency Promet controllability
Visual quality
User study. To present human-aligned assess- ment of MT2V, we further conduct user study involving 20 participants. They are asked to per- form binary voting in 45 one-on-one matchups between our method and baselines in terms of the above-mentionel three aspects. As shown in Fig. 8, our method wins at least 70% matchups for identity consistency and prompt controllability, as well as at least 50% matchups for visual quality. The user study reconfirms the superiority of our method, which echoes the metric results.
Figure 8: User study. The overall high winning rate proves that EchoShot better aligns with human preferences.

---

Consistent: [ATTRIBUTE]
middle-aged
curly, deep
Shot 1: [CAMERA]
60
Shot 2:
[CAMERA]]
[APPARII]
[AMPARE]]]
light blue and white chechaned shirt, collar open, round glasses
light blue shirt, blue tic, white diagond stripes, round glasses
[EXPRESSIOSN]
[EXPRESSSKON]
serious, slightly sorried
focused szyes, slighlly
right head movement, in moonaffir off.fo..ooooooooooooo gray walls with windows. block cobinet with stocked files and folders
and clesing while speokkngg
desk, right hand fist the toble [SACKGROUND]
[EAC KIRROUN)
onother office, veretion blinds. black phone. cobinet. Files
Cur tirieaamp
3.2%
SimpleConcat
no cui, prompts intermingling (apparel. background)
SimpleConeat + TeRoPE
prompls intermingling (apparel, backgroun)) ................................
SimpleConcat (fall)
perfect position, precise prompt adherence
Figure 9: Illustration of generated videos of three ablation models, which confirms that TcRoPE and TaRoPE mechanisms perform expected.
4.3 Ablation Study
To verify the effectiveness of each proposed mechanism, we carry out an ablation study. For rapid assessment, we build reduced-scall dataset for ablation training. We construct three ablation models: (1) SimplyConcat (SC), which concatenates the videos and the captions from different shots indiscriminately, and applies vanilla RoPE. (2) SC+TcRoPE, which only applies the TcRoPE mechanism. (3)
is our full method. As shown in Fig. 9, SC struggles to recognize the difference between shots, causing failure of shot transition. Though occasionally generates multi-shot videos, the shot transition positions are elusive and uncontrolled. With the modeling of shot-wise boundaries, SC+TcRoPE enables shot transitions at given timestamps. Yet. both SC and SC+TcRoPE exhibit intermingling effect between prompts from different shots (e.g.. the clothes are the same across shots, though given different descriptions), likely caused by simple caption concatenation. In contrast, our full method demonstrates precise shot- wise prompt adherence without intermingling.
Conclusion
We introduce EchoShot, novel framework for multi-shot portrait generation that addresses the limitations of existing single-shot pipelines. EchoShot achieves high-quality, identity-consisten multi-shot vidco synthesis with flexible user-defined control over content and appearance through two shot-aware RoPE mechanisms: TeRoPE and TaRoPE. Furthermore, we develop PortraitGala, a large- scale, high-fidelity human-centric video dataset designed to support cross-shot identity consistency and fine-grani -ne-graided controllability. Extensive experiments confirm that EchoShot surpasses existing methods in both quantitative metrics and qualitative assessments. Notably, EchoShot provides promising solution to enhance artistic workflows in real-world applications.
Limitation, Our framework currently lacks the capability directly extend shots from previous content, thereby limiting the generation longer, continuous video segments within single shot. Additionally, generating consistent multi-subject scripts is promising direction for future work.
10

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Acknowledgements
This work was supported in part by NSFC under Grant 62192781, 62172326, 62137002, 62302384, Research Project Funded by the State Key Laboratory of Communication Content Cognition under Grant No. A202403, and the Project of China Knowledge Centre for Engineering Science and Technology.
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EchoShot: Multi-Shot Portrait Video Generation
Appendix
Table of Contents
A Mathematical Formulation
23
A.I Derivation Rotary Position Embedding
23
A.2 Proof of Claim about Attention Score with TaRoPE
24
B Details of PortraitGala
25
B.1 Data Processing
25
B.2 Data Distribution
26
B.3 Dataset Examples
26
More Experimental Details
28
D Extra Experiments
29
D.1 Motivation Verification
29
D.2 More Quantitative Results
29
D.3 Quantitative Ablation Study
30
D.4 Parameter Analysis
30
EchoShot Gallery
31
Clarification about User Study
32
G Societal Impacts and Safeguards
33

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A Mathematical Formulation
A.1 Derivation of Rotary Position Embedding
In Sec. 3.1, we introduce Rotary Position Embedding (RoPE) in vanilla models. Based on it, we propose shot-aware mechanisms, TeRoPE and TaRoPE. Here, provide the complete denotation of RoPE, following [40].
ID-RoPE. The proposed TaRoPE is based on ID-RoPE, which is intended for sequential modality along a single dimension. Given a position-indexed query 4m IR or key vector km R
ID-RoPE can be expressed as:
mm 1D-RoPE(qm m) mm Qm
(6)
k ID-RoPE(K m) Rmm. km
where mm. is the rotary matrix, denoted as:
cos ml sinmo,
- -sin m0
0
0
cos m00m
0
0
0
COS m202
sin m0
0
0
0
sin m02
cos m0g
0
:
:
0
0
cos m8
sin m8
0
0
O
sin ml
COS m
12
with pre-defined parameters 1D {0, = 10000
, E [1,2, ....
Thus, the proposed
TaRoPE in Eq. (3) can be further written as:
TaRoPE (. 8) 1D-RoPE 8.k) Rom.. Pi
(9)
GM
TaRoPE (....) ID-RoPE(K .... s. kk) R K.
(10)
3D-RoPE. The proposed TcRoPE is based on 3D-RoPE, which is an extension ID-RoPE tailored for video modality. Given 3D pososition--ndeeed Qt,h.0 E Rd or key vector k Rd. RoPE can be denoted as:
qr.h.w (0-RoPEE(gr,.oom L, h, w) Rdd R Ht,
(11)
kr,,, 3D-RoPE( (,,,
ww t, h w) R t. t.t ke,kw
(12)
where Rd D ho can be denoted as:
(coa
sla 10
cos (6)
5
0
cow
sin
D
sim.0 10
0
cos h6; lift
sin hō's fuf)
D
hul
hi
B
D
0
sio AB
cos &d
Dos wel
D
sin wd)
cos ut8
wd sin me
sin te
0
cos w9
(13)
with pre-defined parameters Oa {0: 10000 -2(-1) . [1,2, 1.. Thus, the proposed TcRoPE in Eq. (2) can be further written as:
TcRoPE(g.) t, h, w. = R.D-ROPE am t+s.j, h, w) = =R R. T Lahom
(14)
k h. TcRoPE h.. h, w. 3D-RoPP (a 1+8.j, h, w) R dtojj. h.w k
(15)
23

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A.2 Proof of Claim about Attention Score with TaRoPE
In Eq. (4), we give concise formulation of the attention score after TaRoPE with claims. We provide a detailed proof of the claim here. Given queries of the s1-th shot 9s, and keys of the s2-th shot keg we divide them into 2-component pairs along the channel dimension, the inner product of their TaRoPE-enhanced representations can be written as complex number multiplication:
A $1,00 = (Re, hay Io, (R koo)
= Re
9s1 (2::2:2 1 th(as 101000111))0) =2)0
1=0
(16)
$ --
(4 2s + 2141 kez cos (k(s1 s2)0.)
(1s) k0 Ys 2 20+)) ( sin (k(81 82)0s).
where ks,, (2::2+1) represents the 2i (2i + 1)th components of ks and k2 2: represents the 2 component of kg Note that the scalar l without subscripts the mismatch suppression scale. When Os and kig from the same shot (i.e., $1 82), Eq. (16) can be simplified as:
A
(9,,22 ks + qs, k22.2641) = 9s; 1: A8
(17)
which indicates that the attention score with TaRoPE remains exactly the same as that without TaRoPE.
To investigate the behavior of Eq. (16) as k(s1 s2) varies, we follow [40] denote
h; = 413:24+1h/22244 Sy = Di- c( -1)=0 and let hg = 0, So = 0, we can rewrite
Eq. (16) using Abel transformation:
42:21+1 k ((1 (01-
=2)0
Σ
h1 82) =
S (h,-1 - -14).
(18)
=00
1=0
Σ 1 11 (2)-02)0
S (h++1 h:)l
S [1 ha)l
1=0
(19)
$ 4-1
max lhs+1 - hal
S
1=0
|
0
We plot the graph of the value of fz) = ン which shows monotonically decreasing function. By synthesizing the above derivations, arrive at the conclusion of Eq. (4).
- S as x = k(s) - $2) varies in [0,50] in Fig. 10,
24

---

1050
1000
950
900
850
700
650
4.50
400
250
200
150
X
Figure 10: Graph of the attention score (xx as x varies.
B Details of PortraitGala
B.1 Data Processing
We detail the construction of the PortraitGala dataset here, adhering to the methodology outlined in Sec. 3.4. The PortraitGala dataset comprises collection of video resources, drawing from three primary sources: 1) publicly available, high-quality portrait datasets such as CelebV-HQ [57] and CelebV-Text [52]; 2) subsets extracted from large-scale, open-source video datasets including Open Vid- IM [33] and OpenHuman Vid [25]; and 3) a selection of videos obtained from various web- sites, encompassing both films and television series. In its entirety, the original dataset encompassed 26,967 hours of video footage.
The curation process began with rigorous filtering of raw video material to ensure quality and rele- vance. Specifically, we eliminated videos exhibiting vertical aspect ratios or substandard resolution, retaining only those conforming to the criteria: width height > 480 pixels. Given the extended duration of many source videos and the presence of multiple camera shots within them, we employed PySceneDetect [5] segment these videos into discrete, single-shot clips. Furthermore, to maintain high aesthetic quality, clips with an aesthetic score, as determined by [37], below a threshold discarded.
To ensure that each video clip in PortraitGala featured only one individual, we implemented person counting procedure leveraging YOLOv11 [23] for person detection and tracking. Subsequently, person identities were assigned using an improved version of HDBSCAN [32], density-based clustering algorithm, coupled with proprictary facial embedding extraction technique. A remaining challenge was the identification and removal of near-duplicate video clips depicting the same indi- vidual performing similar actions within consistent setting. To address this, we simply employed Mon-Maximun Suppression based on the detected person bounding boxes, specifically for videos with matching person IDs. Through rigorous manual verification, achieve a clustering accuracy exceeding 99%, demonstrating that all video clips assigned identical identifiers consistently depict the same individual throughout.
Finally, attributes pertaining to camera shot composition and shot type were derived using internal analytical methods. Supplementary attribute labels and descriptive text were generated with the aid of Gemini 2.0 Flash [1] using the prompt below.
1. You are now going to describe the film for audience. The description should be as detailed, comprehensive, and logical possible.
2. The output must be in English, around 600 words.
3. Do not use vague words such as "appear to be" or "seem." " Your descriptions must be definitive, precise, and highly accurate.
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4. There is only one main person, and all descriptions revolve around this person.
5. All sentences must be affirmative statements. No questions or negative sentences are allowed.
6. Do not determine the gender of the main character. Use "the person," " "the person is," or "the person's "s" as pronouns.
7. Please provide a description of the entire video, rather than describing each individual image.
8. Each input requires descriptions of the following eight aspects, with each aspect not ex- ceeding 100 words, and the key points of the descriptions between the aspects should not overlap:
(a) Hairstyle: Provide detailed and comprehensive description the hairstyle of the person. Start with 'The person's 's hair
(b) Expression:
Provide detailed and comprehensive description of the expressions and emotions of the person. (If there are any temporal changes in expression, also need describe in detail.) Start with 'The person's expression...
Provide detailed and comprehensive description of the clothing worn by the person (including lower body garments if visible). If there are accessorie (e.g.. glasses, earrings, watches), describe them as well. Start with 'The person wears ...'. (d) Behavior:
(c) Apparel:
Provide a detailed and comprehensive description of the behavior of the person (e.g., actions, interactions with objects). Start with 'The person ...'.
(e) Background: From the perspective of film design, provide detailed and compre- hensive description of the background and environment. Start with 'The background
(f) Lighting: From the perspective of film analysis, provide a detailed and comprehensive description of the overall lighting conditions. "The lighting...
B.2 Data Distribution
We perform a statistical analysis on some key aspects of PortraitGala dataset, depicted in Fig. 11. The shot-scale distribution indicates that most videos are middle-shot (37.8%). followed by close-shot (33.6%), long-shot (15. (15.9%) and full-shot (12.7%). In terms of gender distribution, male individuals predominant at 56.7%, while female individuals constitute 39.7% %, and others make up 3.6%. The age-group distribution shows a vast majority youth individuals 74.9%, with middle-aged individuals at 22.6%, and other age groups at 2.5%. Finally, ethnicity distribution reveals that 51.2% of the shown individuals are white people, 19.3% are black people. 11.4% are Asian people, and 18. % are other ethnicities. This statistic breakdown highlights the diversity present in the PortraitGala dataset.
Shot-scale distribution
Gender distribution Other
Age-group distribution Other
Ethinicity distribution
Long-shot
Other
Middle-aged
Close-shot
33.6%
15.9%
12.7%
Full-shot
Male
56.7%
6%6
39.7%
Female
2.5%
22.6%
White people
51.2%
18.1%
19.3%
Black people
74.9%
37.8%
11.4%
Youth
Middle-shot
Asian people
Figure 11: Distribution of key aspects of PortraitGala.
B.3
Dataset Examples
To provide clear and intuitive representation of the dataset, we present a gallery multi-shot video instances in Fig. 12. It highlights the diversity of the video distribution and the granularity of the captions.
26

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is zoom-our middle-shet clip Caucasion, youth,
The huirstyle is long, straight, and dark brown. It parted centrally and cascades smoothly over hoth shoulders, extending past the chest.
appare is light heige corduray jacket collar, froet, rwo flup chest pockets, Undeementh, light green visible, featuring red frim around iss nockline.
expression consistently neutral composed. gaze direct towards camern steady, open eyes. mouth closed, lips
slightly pressed logetherr conveying serious and locused demeanor throughout the clip.
The behavior is standing perfectly still and upright in the center of the frame. There are no discemible movements the body limbs. The person cameera without interaction with objects.
maintains unwavering direct
The background large, dark chalkboard densely covered with white chalk. disphays complex mathematical cquations, scientific graphs like a
normal distribution curve, and various geometric polteens. The setting significs academic scientific ironment.
The lighting is warm, golden light emanates from a dense array of exposed light bulbs positiense above. A direct key light illuminates the person's face from the front. The overall lighting bright and focused on the person, with the backegroudd also visibly lit.
Figure 12: Illustration of examples PortraitGala.
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C More Experimental Details
All the training is carried on NVIDIA A10 SOGB GPUs. The MT2V pretraining takes 3,500 GPU hours while the PMT2V and InfT2V take additional 2000 and 1000 GPU hours, respectively. Throughout the training, all the important settings are listed below:
Parameter
Value
Video height
480
Video width
8322
Video frame
125
FPS
16
Batchsize
Train timesteps
1000
Train shift
5.0
Optimizer
Adam W
Learning rate
8e-6
Weight decay
0.001
Sample timesteps
50
Sample shift
5.0
Sample guidance scale
5.0
Table 2: Experimental settings of EchoShot.
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D Extra Experiments
D.1 Motivation Verification
The pivotal core of our method is the multi-shot text-to-video modeling with the proposed two mechanisms, TcRoPE and TaRoPE. To affirm motivation well as verify the effectiveness, conduct an intuitive experiment between EchoShot and the vanilla model. To specify, we perform three-shot portrait video generation with EchoShot given three timestamps and three prompts. For the vanilla model, we concatenate the three prompts together as a input and perform standard generation. During generation, We collect the self-attention scores and cross-attention scores of the DiT blocks, average them and plot the heatmaps. As shown in Figs. 13(a) and (b), the vanilla model indiscriminately allocates attention to the visual tokens and the textual tokens, which is suitable for single-shot video modeling. Yet in the context of multiple shots, the vanilla model fails to differentiate the shots. By contrast, as shown in Fig. 13(c), TcRoPE establishes inter-shot boundary during self-attention calculation, with the extra rotation naturally reduce the interactions of features from different shots while enhance that from the same shot. Additionally, TeRoPE allows the model to flexibly allocate attention to potentially crucial pixels, whether from the same shot or from different shots. Fig. 13(d) confirms similar conclusion to the above one, in the context TaRoPE and cross-attention.
Shol
Shot
Shot
Prompt
Promut
Prompt
Shot 1-
Shot 1-
Shot 2
Shot 2
Shot 3-
Shot
Self-attention without TcRoPE
Cross-attention without TaRoPE.
Shot
Shot
Shot
Prompt
Prompt 2
Prompt
Shot
Shot 2
Shot 2-
Shot
Shot
(e) Self-attention score
(d) Cruss-attention score with TaRoPE
Figure 13: Visualization of self-attention score matrix w/ and w/o TcRoPE and cross-attention score matrix w/ and w/o TaRoPE.
D.2 More Quantitative Results
In Tab. 1, we present the quantitative results. supplement, we propose the stand deviation results of these metrics across the test set here.
29

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Identity consistency
Prompt controllability
Visual quality
Method
FaceSim-Are FaceSim- Cur
App.
Mot.
Bg.
Sta.
Dyn.
StoryDiffusion+-ann
68.65
65.29
83.33
72.50
69.53
63.20
84.68
CeLLRA+Wan--ee
68.45
65.04
87.59
83.71
75.37
79.49
87.98
MldnyannVideo- 13B (2 shots)
64.80
61.25
83.39
68.15
72.37
86.37
87.22
EchoShot- (ours)
73.74
69.43
95.84
88.86
94.72
88.91
87.85
Identity consistency FaceSim-Cur
Prompt controllability
Visual quality
Method
MiceSii-Are
App.
Mot.
Bg.
Sta.
Dyn.
StoryDiffusion+ Wan-14B
6,24
6.86
9.50
22.47
21.29
15,20
2.57
CC-LoRRAWWa--444
7.02
6.75
10.16
18.41
19.77
13.08
2.68
Video-133 (2 shots)
9.16
8.28
10.13
18.35
17.95
5.76
3.51
EchoShot-IB (ours)
7.13
7.91
6.51
11.25
6.68
4.90
2.77
Table 3: Metric results (mean and standard deviation) in MT2V task.
Identity consistency
Prompt controllability
Visual quality
Method
Cut control
FaceSim-Are
FaceSim-Cus
App.
Mot.
Bg.
Sta.
Dyn.
75.24
70.61
55.79 59.16
47.56
81.90
75.24
SC+TcRoPE
74.77
69.85
81.72
72.84
74.22
84.32
81.69
SC+TcRoPL+ ((ll)
Yes
75.83
70.58
94.12 87.41 93.96
84.10
83.46
Table 4: Metric results of three ablation models on reduced-scale dataset.
D.3
Quantitative Ablation Study
We further conduct quantitative ablation study following Sec. 4.3 Tab. 4. Benefiting from the curated high-quality multi-shot dataset, the identity consistency and visual quality of three ablation models exhibit minimal variation. terms of prompt controllability. the full model demonstrates significant advantages over the comparing models, echoing the qualitative results. The ablation study confirms that TeRoPE and TaRoPE mechanisms perform as expected.
D.4 Parameter Analysis
Our method relies primarily on two key hyperparameters, the phase shift scale j in Eq. (2) and the mismatch suppression scale & in Eq. (3). To investigate the appropriate parameter selection, conduct an intuitive parameter analysis with the reduced-scale dataset. We leverage the mathematical analysis and empirically pick several typical values, j E (2, 4, 6), k 6 and j = 4, k (2, 6, 12). The quantitative results of the models under different settings are shown in Tab. 5. To summarize, too small j excessively enhances the inter-shot visual interactions, leading occasional cut failure and prompt intermingling with low prompt controllability scores. While too large nearly blocks interactions, causing identity inconsistency with low identity consistency scores. Similarly, a too small & gives rise to prompt intermingling while too large k results in a slightly decrease in scores.
Consolidating the results, we choose j = k = 6 as the default setting.
Identity consistency
Prompt controllability
Visual quality
Method
FaceSim-Are
FaceSim-Cur
App.
Mot.
Bg.
Sta.
Dyn.
j=2,k=6
75.45
70.64
88.75
82.10
85.28
80.97
81.67
6=
70.19
67.30
94.25
88.29
92.66
83.18
82.03
44kk2
75.12
69.73
84.20
76.69
80.16
80.55
81.02
j 4.k=12
74.98
69.45
92.15
92.73
83.50
83.91
(ours)
75.83
70.58
94.12
87.41
93.96
84.10
83.46
Table 5: Metric results of different parameter settings reduced-scale dataset.
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E
EchoShot Gallery
go
80
go
80
g
8
d0
g0
B0
Figure 14: Illustration of generated videos by EchoShot.
31

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Clarification about User Study
Instructions for Participants
Thank you for participating in our study!
We appreciate your willingness to contribute to our research. Below, you will find detailed instructions on how to complete the tasks involved in this study. Please read this information carefully before beginning.
1. Purpose of the Study
The goal of this study to evaluate different methods for generating multi-shot portrait videos showing the same identity. Your feedback will help evaluate which method produce videos with best quality. Your participation is highly valuable to us!
2. Task Overview
You will be asked to compare two methods by evaluating their outputs in series one-on-one matchups. Each matchup will present results generated by our proposed method and random baseline method (both anonymous). Your task is to vote for your preferred option each matchup based on the following criteria:
Identity Consistency: Which method better preserves the same identity of the human (e.g.. facial features, expressions, and overall appearance) across all frames and shots in the video?
Prompt Controllability Which method more accurately follows the given prompts (e.g., appearance, motions, expressions, and background) in the generated video?
Visual Quality:
Which method produces a video with higher visual quality, considering factors like sharpness, smoothness of motion, and absence of artifacts (e.g., blurriness or unnatural textures)?
3. How to Complete the Task
You will complete 45 matchups in total.
For each matchup, you will see two outputs side by side.
To cast your vote, click on the button corresponding to your preferred option.
There are no right wrong answers. Please choose the option that feels best to you.
4. Time Commitment
The study should take approximately 20-30 minutes to complete. You can work at your own pace, but we recommend completing the task in one sitting to ensure consistency.
5. Voluntary Participation
Your participation in this study is entirely voluntary. You are free to withdraw at any time without penalty explanation. If you decide to withdraw, your responses will not be included the analysis.
6. Compensation
This study does not provide financial compensation. However, your contribution is greatly appreciated and will directly support advancements in this field of research.
7. Privacy and Data Use
Your responses will be anonymized and used solely for research purposes.
No personally identifiable information will be collected or stored.
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8. Questions or Concerns
If you have any questions about the study encounter technical issues, please contact us. We are happy to assist you.
9. Consent
By proceeding with the study, you confirm that:
You have read and understood the instructions.
You agree to participate voluntarily.
You understand that you can withdraw at any time without penalty.
Thank you again for your time and effort. Let's get started!
Compensation. Participants were informed that their participation was entirely voluntary based on their willingness contribute to scientific research, without financial compensation. No incentives or pressures were applied to encourage involvement. Participants could withdraw at any time.
Minimal risks. Before beginning the study, participants about the purpose of the research, and their rights as participants. Consent was obtained from all participants prior their involvement. The risks associated with this study were minimal. The tasks posed no physical, psychological, or emotional harm to participants. Participants were only required perform binary voting tasks, which involved evaluating outputs generated by different methods. All interactions were conducted through a secure online platform, ensuring privacy and anonymity. No sensitive data personally identifiable information was collected during the study.
provided with detailed information
Approval. This study received approval from the research institution.
G Societal Impacts and Safeguards
The advancements in multi-shot portrait video generation, as exemplified by our work on EchoShot, present profound societal impacts across numerous domains. By enabling high-quality, identity- consistent, and content-controllable portrait video creation, EchoShot democratizes access ad- vanced video production tools, empowering creators of all skill levels to produce professional -gradd visual media. This innovation streamlines workflows in industries such as filmmaking, advertising, virtual avatars, and social media content creation, leading to significant improvements in efficiency, cost savings, and creative flexibility. The ability to generate consistent multi-shot videos with fine-grained control over attributes like facial expressions, outfits, and motions has the potential to revolutionize storytelling, personalized media, and digital human modeling.
However, the rapid adoption of such generative technologies also raises important challenges. Job for traditional video editors and artists who rely on conventional methods, necessitating reskilling and adaptation to remain competitive in an evolving job market. Ethical con- cerns are equally critical, as the misuse of generated videos (e.g.. creating deepfakes, impersonations. or misleading content) can undermine public trust and exacerbate misinformation. Ensuring that models like EchoShot are trained unbiased datasets essential to prevent the reinforcement of harmful stereotypes or biases in generated content. To address these issues, we inherit safeguards from foundational models 1an2.1, including mechanisms for detecting inappropriate or harmful outputs, while adhering to strict usage guidelines. Furthermore, by open-sourcing our models and dataset, we aim to foster transparency, encourage responsible use, and facilitate community-driven improvements in ethical generative Al development.
displacement may
33


Reviews:
[{"review_id": "SuNVJ7NuAb", "reviewer": "Reviewer_Ttb3", "summary": "EchoShot is a system for multi-shot portrait video generation with subject-identity consistency and shot-wise controllability. Built on a 1.3 B-parameter Wan-DiT backbone, the framework introduces two shot-aware rotary-position-embedding variants: Temporal-Cut RoPE (TcRoPE) for marking inter-shot boundaries in self-attention and Temporal-Align RoPE (TaRoPE) for suppressing cross-shot leakage in text-video cross-attention.Training is driven by PortraitGala, a newly curated 600 k-clip dataset (400 k identities, 1 k h video) with fine-grained captions Three tasks are demonstrated:\nMT2V - text-to-multi-shot video;\nPMT2V - personalised multi-shot generation from a face reference;\nInfT2V - unbounded shot extension via a disentangled RefAttn.\n\nOn a private 100-prompt benchmark,  EchoShot improves identity consistency and prompt alignment over keyframe pipelines and HunyuanVideo, while users prefer its outputs in >70 % pairwise votes. All code and data are promised for open-source release upon acceptance", "questions": "1. Full release of the evaluation set and outputs.\nWill you publish the entire 100-prompt test set together with your inference videos and metric scripts?\nA public release before the rebuttal deadline would let reviewers and future work verify your claims.\n\n2. Concrete timeline for open sourcing code, weights and PortraitGala.\nPlease specify an exact date (e.g., “on or before 1 October 2025”) for releasing training code, inference checkpoints and the PortraitGala dataset (or a legally shareable subset).\nA firm commitment would resolve reproducibility concerns.\n\n3. Public benchmarks. Could you run EchoShot and baselines on an open set (e.g., a 20-clip WebVid subset) and publish the numbers? Demonstrating similar gains outside your data would markedly strengthen the work.\n\nBased on author's answers, I'm willing to either increase or decrease my scores.", "limitations": "The paper notes that EchoShot cannot yet extend an existing shot into longer continuous footage. It should also spell out potential misuse for non-consensual identity swaps and describe intended safety mechanisms; an explicit plan would be welcome.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\nSubstantial gains in automatic metrics and a user study confirm that TcRoPE + TaRoPE preserve identity across shots and respect fine-grained prompts. The idea of embedding shot indices directly into 3-D and 1-D RoPE is conceptually simple yet addresses variable-length shots and cross-shot prompt bleed-over in a unified way. \nArchitecture diagrams, loss formulas and hyper-parameters are clearly documented; compute cost (3 500 A100 GPU-h) is disclosed \nMulti-shot portrait generation is a frequent real-world request (story-boarding, virtual try-on, character vlogging). By training natively on many-to-one clips, EchoShot sets a useful precedent beyond iterative keyframe hacks.\n\nWeaknesses:\nAll metrics rely on an unreleased 100-prompt set; no numbers are reported on public corpora. Reproducibility therefore hinges on a future release.\nLimited baseline breadth. Comparisons omit recent multi-event generators and restrict closed-source Kling to PMT2V only, leaving the competitive landscape unclear.\nConditional openness. Code, checkpoints and PortraitGala will be released “upon acceptance”, not at submission, delaying external validation.", "was_revised": true, "final_justification": "I am satisfied with the author's response and will advocate for the acceptance of this paper.", "rebuttal": "__We are so grateful that all the reviewers give our work positive initial ratings (4454)__, which is a tremendous encouragement for us. We particularly thank you for the insightful feedback and for highlighting the strengths of our work. Our primary motivation is to contribute to the video generation community, so we are willing to take any action that enhances the openness and impact of our work. For each of the questions, we provide a detailed clarification below.\n\n---\n\n## Q1: Evaluation Set\nThank you for highlighting the importance of an open evaluation set. We agree that this is crucial for enabling future work to compare against and build upon our method. Thus, __we promise to release our full evaluation set, video results and evaluation pipeline__.\n\nTo provide immediate clarity on our evaluation process, we would like to __share some key details here__. The Vision-Language Model (VLM) used for our automated scoring is Gemini-2.5 Pro. For the *Prompt Controllability* metrics, the VLM scores the generated videos across nine distinct aspects: ethnicity, gender, age group, hairstyle, apparel, expression, action, background, and lighting. The final scores are calculated as follows: *App. score*: the average of scores for ethnicity, gender, age group, hairstyle, and apparel. *Mot. score*: the average of scores for expression and action. *Bg. score*: the average of scores for background and lighting. For the *Visual Quality* metrics, the *Sta. score* and *Dyn. score* are directly scored by the VLM.\n\nDue to the format limitations of the rebuttal, we can only __provide a prompt example from our evaluation set and the scoring instructions input to Gemini-2.5 Pro (please see the postscript)__. The full benchmark will be released according to the timeline below.\n\n## Q2: Timeline for Open-Sourcing\nWe have established a clear open-sourcing timeline. In fact, to facilitate early access, we have already finished certain steps:\n\n- Before June 15th, 2025: Launch project page and generation gallery. __(Finished)__\n- Before July 15th, 2025: Publicly release codes. __(Finished)__ \n- Before July 15th, 2025: Publicly release the model weight. __(Finished)__\n- Before August 15th, 2025: Publicly release the full test set, evaluation details, and baseline benchmarks.\n- Before September 30th, 2025: Publicly release the PortraitGala dataset.\n\nWe are committed to this schedule and believe it directly addresses the concerns regarding reproducibility.\n\n## Q3: Evaluation on Public Benchmarks\nThe primary reason for not including evaluation on public benchmarks was __the lack of a public a large-scale dataset with many-to-one annotations__ (i.e., multiple video shots of the same person paired with consistent text descriptions), to the best of our knowledge. This very gap is exactly the reason why we construct and commit to releasing the PortraitGala dataset. Regarding your specific suggestion of WebVid, it consists of diverse scenes and is designed for general-purpose video generation. It __lacks sufficient videos and annotations of the same person in different contexts__. As a result, we are unable to conduct training or evaluation on WebVid in the multi-shot portrait generation setting. For this issue, __we promise to release PortraitGala dataset, detailed evaluation methodology, and the videos generated by EchoShot and all baseline models__. By doing so, we will establish the first public benchmark specifically for the multi-shot portrait generation task. We believe this contribution will markedly strengthen our work, provide a lasting resource for the community, and address your concerns about comparative evaluation.\n\n---\n\nWe believe the rebuttal stage is highly meaningful, and we will add the rebuttal discussion to the new version of our paper as much as possible. We would be very grateful if you would consider raising your score in light of these clarifications.\n\n---\n\n### Postscript 1: An Example of 100-prompt Evaluation Set\n> [Shot 1] Camera pans left, medium shot. This segment shows a Caucasian, male, middle-aged person. The character's hair is dark brown, medium length, voluminous, slightly fluffy, with natural waves, and some hair falls over the forehead and sides of the face. The character is wearing a light blue long-sleeved shirt, with the collar open, and a dark blue striped tie. The character's expression is serious, with a focused gaze, corners of the mouth slightly downturned, seemingly thinking or observing. The character's action is standing, body slightly angled to the left, head turned to the right, seemingly observing or listening, with hands naturally hanging down and no obvious body movements. The video scene is an office, with a light green background wall. On the left side, there is a whiteboard covered with names and school names, and on the right wall, a white bulletin board is hanging. The video lighting is generally dim, with light coming from the left, casting shadows on the character's face, creating a serious and oppressive atmosphere.\n\n> [Shot 2] Still shot, close-up. This segment shows a Caucasian, male, middle-aged person. The character's hair is dark brown, slightly long, layered, naturally combed back, with some hair falling on both sides of the forehead. The character is wearing a light blue and white striped shirt, with the collar open, revealing the collarbone. The character is wearing a dark blue tie with white polka dots. The character's expression is calm, slightly serious. The character's action is sitting on a sofa, body slightly leaning forward. The character occasionally makes gestures with both hands. The video scene is indoors, with a light green wall in the background, which has subtle textures. The scene is simply arranged, without excessive decoration. The video lighting is soft natural light, with light coming from the front, illuminating the character's face and upper body. The light is evenly distributed, with no obvious shadows. The overall lighting conditions are relatively bright.\n\n> [Shot 3] Still shot, close-up. This segment shows a Caucasian, male, middle-aged person. The character's hair is dark brown, slightly fluffy, naturally combed back, with slightly longer sideburns. The character is wearing a light blue Hawaiian shirt, with white and dark blue patterns printed on it, and the collar slightly open. The character's expression is relaxed and slightly smiling, with a focused gaze, seemingly listening or thinking. Occasionally, the corners of the mouth subtly turn up, showing a sense of calmness and confidence. The character's action is slight head movements, subtle lip movements, seemingly speaking, with the body remaining relatively still, shoulders relaxed, and hands possibly placed on a table or in front of the body. The video scene is an indoor office, with a blurred background. Some stacked files and office supplies are visible. The video lighting is soft natural light, with light coming from the front, illuminating the character's face. Shadows are not obvious, and the overall light is even, creating a comfortable and relaxed atmosphere.\n\n### Postscript 2: Rating Instruction for Prompt Controllability Metrics\n> Please play the role of a video generation model evaluator. I will give you a prompt and a corresponding generated video clip. Please judge whether the content of the video clip meets the description of the prompt in terms of ethnicity, gender, age group, hairstyle, apparel, expression, action, background, and lighting. If it fully meets the description, output 100, if it does not meet the description, output 0, and if it partially meets the description, give a score between 0 and 100 according to the degree. Each aspect should be judged independently, and the entire content of the prompt must be strictly considered. Only give the results, written in the form of a python dictionary, with the key being the evaluation aspect and the value being the score.\n\n### Postscript 3: Rating Instruction for Visual Quality Metrics\n> Please play the role of a video generation model evaluator. I will give you a prompt and the corresponding generated video clip. Please rate the video, which is divided into two scores, static and dynamic, each from 0 to 100. The static dimension (single-frame picture quality) is considered from the aspects of clarity and sharpness, color performance, detail restoration ability, noise and artifact control, composition and aesthetics, resolution consistency, etc. The dynamic dimension (continuous picture performance) is considered from the aspects of motion smoothness, motion blur processing, timing consistency, dynamic range and brightness changes, action realism, scene transition and occlusion processing, dynamic detail retention, etc. Only give the results, written in the form of a python dictionary, with the key being the evaluation aspect and the value being the score."}, {"review_id": "Mp45WgZz0E", "reviewer": "Reviewer_znTz", "summary": "The paper introduces a novel task of multi-shot portrait video generation where the portrait of a single person is consistent identity-wise across all of the shots. \n\nContributions are as follows: \n\n(1) PortraitGala, a large-scale portrait dataset comprising 250k one-to-one single-shot video clips and 400k many-to-one multi-shot video clips. \n\n(2) A new position embedding mechanism termed Temporal-Cut RoPE (TcRoPE) is introduced to resolve challenges in variable shot counts, flexible shot durations, and inter-shot token discontinuities. \n\n(3) Temporal-Align RoPE (TaRoPE) (in the video-text cross-attn module) is introduced for better text control. \n\n(4) An external face encoder enables reference-image based personalized multi-shot video generation (PMT2V).\n\n(5) a disentangled RefAttn mechanism is used for infinite shots video generation (InfT2V).", "questions": "I would like to know (1) if the proposed method can generalize to videos that contain non-photorealistic characters, (2) what are the limitations considering camera view/rotation, (3) is there a way for the method to be extended to multiple people in one shot or to generate videos with multiple persons where at least one of them is consistent across the shots?\n\n(4) Generated videos (supmat) seem static and the quality in the eyes and teeth regions is low. Also, it seems there are issues with the overall image quality in the first few frames. Why?\n\n(5) Lines 42/43: Can the authors explain \"the bottleneck\" effect in more detail?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThe problem is interesting. Generating multiple videos of the same person with fine-grained control is important for applications in the emerging creative AI filmmaking industry.\n\nThe method is evaluated well, especially considering qualitative and quantitative comparisons with the baselines. There are also additional results and ablation studies in the appendix.\n\nDataset contribution seems significant.\n\nPaper is well written.\n\n\nWeaknesses:\n\nLines 42/43: Can the authors explain \"the bottleneck\" effect in more detail?\n\nCan the camera rotate around the person (360 degrees) or is the method limited to frontal shots only?\n\nCan the framework be extended to multiple people?\n\nDoes the method work for photorealistic examples only or does it also generalize to out-of-distribution domains? \n\nGenerated videos (supmat) seem static and the quality in the eyes and teeth regions is low. Also, it seems there are issues with the overall image quality in the first few frames. Why?\n\nI would suggest adding more important ablation studies (model) to the main paper.\n\nIt would have been helpful to see all of the videos that were used in the user study.\n\nThere are typos in some parts:\n\n- Section 4: Experiment -> Experiments\n\n- Figure 6: “not follow” -> doesn’t follow", "was_revised": true, "final_justification": "I would like to thank the authors for the rebuttal. I think that this work is interesting, well evaluated, and the dataset contribution seems significant. I also appreciate that the authors have mentioned that the method can generalize to certain out-of-the-distribution domains and noted the limitations/future directions. My main concern with the current version remains the issues of video quality consistency and artifacts noted earlier. For this reason, I am keeping my original rating, but given the strengths of the work, I recommend acceptance as a poster.", "rebuttal": "__We are so grateful that all the reviewers give our work positive initial ratings (4454)__, which is a tremendous encouragement for us. We particularly thank you for the meticulous feedback and for highlighting the significance of our work. For each of the questions, we provide a detailed clarification below.\n\n***\n\n## Q1: Generalization to Non-Realistic Styles\nThough our training dataset predominantly consists of videos in a realistic style, we have observed that EchoShot exhibits __an emergent capability to generate consistent shots in certain artistic styles__, such as 3D animation. We are sorry that due to rebuttal policy, we cannot include the corresponding video examples. To explain this phenomenon, we hypothesize that EchoShot effectively __inherits the versatile generative capabilities of the base mode__ (i.e., Wan-1.3B), benefiting from our proposed RoPE designs. However, due to the lack of explicit training on these artistic domains, the visual quality of these stylized generations is not yet on par with that of the realistic style. Furthermore, __for styles that are beyond the capability of the base model itself__, such as stop-motion animation, EchoShot is consequently unable to generalize.\n\n## Q2: Camera Control\nOur dataset annotation and model generation capabilities focus on __common camera movements__, including static, handheld, pan left/right, tilt up/down, zoom in/out, limited-angle orbiting shot. Consequently, the model currently __does not support more complex camera motions__, such as 360-degree orbiting shots or fully customized camera paths. We acknowledge this as a current limitation and a valuable direction for future research.\n\n## Q3: Extension to Multi-Character\nThis is an excellent question that we have also discussed ourselves. Rethinking EchoShot, it is __inherently adaptable to general multi-shot scenarios including single character, multiple characters and even objects__. The consistency across shots can be captured by introducing specific semantic patterns as indicators. Then EchoShot can automatically learn the correspondence between these semantics and the generated visual content. For your consideration, here we propose one potential solution for the multi-character scenario: __annotate each character in text prompts with a unique identifier__. For instance:\n\n> [Shot 1] (PERSON 1: a man with black hair) is running in a park …\n\n> [Shot 2] (PERSON 1: a man with black hair) is walking on the beach with (PERSON 2: a young boy wearing a blue shirt) ...\n\n> [Shot 3] (PERSON 2: a young boy wearing a blue shirt) is talking to (PERSON 3: a woman in a white dress) ...\n\nThen EchoShot could be trained directly on a dataset structured this way. We consider this a very promising direction for future work.\n\n## Q4: Video Quality\nArtifacts do occasionally occur during generation, which __we attribute to the limited parameters of the base model__. Wan-1.3B is a very small model compared to peer models. Our internal tests confirm that __original Wan-1.3B exhibits inferior performance to original Wan-14B in terms of dynamic quality and face details__. Considering the computation cost, we decided to conduct this study on Wan-1.3B. Though, the method we proposed can be seamlessly applied to larger models. For future work, we plan to __explore parameter-efficient fine-tuning methods__ (e.g., LoRA) to adapt EchoShot to the 14B model at a lower cost. Based on the current results, we are confident that migrating to a more powerful base model would achieve even higher quality.\n\n## Q5: Explanation of the Bottleneck Effect\nWe use the term *the bottleneck effect* to describe a phenomenon in *keyframe-to-video synthesis pipelines*. These cascaded pipelines typically consist of a consistent text-to-image (CT2I) model followed by an image-to-video (I2V) model, where the output of the former serves as the input for the latter. In this architecture, the quality of the final video generated by the I2V model is heavily constrained by the quality of the initial keyframes produced by the CT2I model. Thus, __the performance of the CT2I model acts as a \"bottleneck”, limiting the overall system's potential, regardless of the I2V model's inherent capabilities__. This effect is empirically validated in Figure 5 of our paper. Both \"StoryDiffusion+Wan\" and \"IC-LoRA+Wan\" use the same I2V model (Wan-14B-I2V) and different CT2I models (StoryDiffusion and IC-LoRA), yet their resulting visual quality differs dramatically. This disparity arises because IC-LoRA, as a CT2I model, exhibits superior overall performance compared to StoryDiffusion. In this case, the capacity of Wan-14B-I2V is underutilized.\n\n## Q6: Ablation Study\nThanks for your advice. We will revise the paper to add critical ablation studies into the main part to strengthen our experimental analysis.\n\n## Q7: User Study Videos\nThe supplementary material (zip file) contains video examples generated by EchoShot for user study. We apologize that due to the policy, we are unable to provide videos generated by baseline methods in this rebuttal. To enhance reproducibility and contribute to the community, __we promise to publicly release our full evaluation set, the detailed evaluation methodology, and the visual results from EchoShot and all baseline models__. Our goal is to establish the first comprehensive benchmark for multi-shot portrait video generation, which we believe will significantly strengthen our work and be a valuable resource for future research.\n\n## Q8: Typos\nWe will perform a thorough proofread of the entire manuscript to correct these and any other errors.\n\n***\n\nThanks again for your efforts. We believe the rebuttal stage is highly meaningful, and we will add the rebuttal discussion to the new version of our paper as much as possible. We would be greatly grateful if you would consider raising your score in light of these responses."}, {"review_id": "gokvF00RTC", "reviewer": "Reviewer_44zS", "summary": "The authors of this paper propose EchoShot, a diffusion-based portrait video generation method capable of generating multiple shots with consistent identity. Videos are generated from text prompts and can be further personalized based on the appearance encoded in a facial image. The authors address two tasks: PMT2V and InfT2F. The objective of PMT2V is to personalize the generated shots using ID information encoded in a facial image. The objective of InfT2F is to generate an infinite number of shots with the same identity. The authors propose fixing the prompts and generation of the first shot while varying the prompts of the remaining shots. They propose RefAttn, which enables inter-shot attention while fixing the first shot to ensure ID consistency. The authors also propose modified RoPE position embeddings (TcRoPE and TaRoPE) to enable multi-shot training. Their method is trained on a new dataset, PortraitGala, which comprises multi-shot video clips of the same identity and is thus well-suited for this purpose. Furthermore, the authors plan to release the dataset, which will be a significant benefit to the community.  Their method generates impressive results, surpassing SOTA methods in identity consistency, prompt controllability, and visual quality. Additionally, a user study highlights that EchoShot better aligns with user preferences than SOTA methods.", "questions": "- What is the runtime for generating videos?\n- How do you select the face image for PMT2V during training?\n- Why does the generated face in Figure 6 (right block) not match the appearance of the input face? Ideally, only the hairstyle should change based on the text prompt.", "limitations": "The authors discussed limitations but not potential negative societal impacts.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- The paper is well written and meets the standards of NeurIPS.\n- The method outperforms state-of-the-art video diffusion models for portrait video generation.\n- The proposed method allows for the generation of multi-shot videos with excellent ID consistency, which is highly valuable.\n- The method achieves excellent ID consistency across different shots generated of the same person.\n- The dataset will be made publicly available, which is a significant contribution because it is designed to improve cross-shot identity consistency.\n- The results look very natural.\n- The user study supports the authors' claims.\n\n\nWeaknesses:\n\n- To achieve the desired cross-shot ID consistency, a replicate of the first shot must be generated in every inference attempt.\n- The generated appearance does not match the face input when the hairstyle in the text prompt differs from the hairstyle in the face image. In the right block of Figure 6, EchoShot does not generate shots that align with the person's overall appearance in the face image. In this particular example, I would expect only the hairstyle to be altered. Although the generated appearance of the multiple shots seems consistent, it does not match the input face image. In comparison, the hairstyle prompt matches the face input in the left block, leading to generated shots that match the face input image.\n\n\nSince there are no video results, I cannot judge the temporal consistency or realism of the generated videos.", "was_revised": true, "final_justification": "The authors addressed all points to my satisfaction. Therefore, I continue to support the acceptance of the paper and my positive rating remains unchanged.", "rebuttal": "__We are so grateful that all the reviewers give our work positive initial ratings (4454)__, which is a tremendous encouragement for us. We particularly thank you for your recognition and for highlighting the overall contributions of our work. For each of the questions, we provide a detailed clarification below.\n\n***\n\n## Q1: Inference Runtime\nThe inference time of EchoShot is __identical to the original Wan-1.3B model__, because our proposed RoPE design introduces no additional computational overhead. To specify, generating a multi-shot video of __8 seconds in total length at 480p resolution__ requires __24GB of VRAM__ and takes approximately __360 seconds__ on a single NVIDIA A100 GPU.\n\n## Q2: Faces for PMT2V Training\nWe assume you are asking about __the construction process of our PMT2V dataset__. For each video clip, we employ the following multi-stage process to obtain the face:\n- We use a face detection model to extract face crops from every frame.\n- These crops undergo a rigorous filtering process based on multiple criteria, including face orientation, sharpness, and the relative size of the face within the frame. The filter is driven by in-house analysis tools.\n- We randomly sample at most 5 face images from the filtered crops, ensuring a minimum temporal gap of 1 second between any 2 face images.\n\n## Q3: Face Consistency\nRegarding the Emma Watson example in Figure 6, the perceived imperfect match between the generation and the input can be attributed to both objective and subjective factors. \n- __Objective Factor: The Inherent Trade-off between Identity and Motion__. There is a common challenge in personalization tasks involving the trade-off between preserving identity and generating dynamic motion. A single reference image input provides limited information, making it __difficult to perfectly model the full spectrum of a person's facial dynamics__. Our model, trained on a diverse dataset, is designed to excel at generating a wide range of dynamic features, such as varied expressions and head poses. In the Emma Watson example, EchoShot generates complex facial dynamics (e.g., laughing, crying) while baselines fail to achieve. This strong emphasis on motion can __lead to subtle deviations from the static input image__, which is perceived as an imperfect identity match.\n- __Subjective Factor: Perceptual Sensitivity to Celebrity Faces__. Human perception is more sensitive to inconsistencies in the faces of well-known celebrities. This is because we have strong prior knowledge of their appearance from various angles and in diverse contexts. We once conducted a small-scale comparative user study, where we provided videos generated by EchoShot based on celebrity faces and non-celebrity random faces. We found that __participants generally perceived a higher degree of identity consistency when seeing generations of non-celebrity faces__.\n\nWe believe this challenge can be effectively mitigated by incorporating multiple reference images to provide a more comprehensive identity representation. We consider this a promising direction for future work.\n\n## Q4: Video Result\nWe included video examples of EchoShot __in the supplementary materials (zip file)__. We believe these results effectively demonstrate the strong temporal consistency and excellent realism achieved by our model. Besides, __we promise to publicly release our full evaluation set and the visual results from EchoShot and all baseline models__. Our goal is to establish the first comprehensive benchmark for multi-shot portrait video generation, which we believe will significantly strengthen our work and be a valuable resource for future research.\n\n***\n\nThanks again for your efforts. We believe the rebuttal stage is highly meaningful, and we will add the rebuttal discussion to the new version of our paper as much as possible."}, {"review_id": "dEVk0igpZG", "reviewer": "Reviewer_SaBb", "summary": "This paper introduces a framework for multi-shot portrait video generation that addresses the limitation of existing text-to-video models in maintaining identity consistency across multiple shots. Built on a diffusion transformer (DiT) architecture, the proposed method incorporates shot-aware position embedding mechanisms to model inter-shot relationships and align text-video content. The proposed method can be extended for reference-based personalized multi-shot generation and infinite shot synthesis. To train their model, the authors collect a large-scale multi-shot portrait video dataset. Experimental results demonstrate superior performance in identity consistency, prompt controllability, and visual quality compared to baselines.", "questions": "* Line 199: new patter -> new pattern\n\n* It is unclear whether the provided videos in the supplemental materials are generated **with face ID condition (Sec. 3.3 PMT2V) or not**.", "limitations": "The authors discussed the limitations in Line 297-299, but the discussion is too brief. I think it would be better if the authors addressed the limitations in more details in the appendix.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n* Different from existing single-shot t2v works, this submission focuses on the multi-shot portrait video generation (MT2V) task,  addressing a practical need in content creation workflows. \n\n* The proposed method does not introduce any architecture changes to the pre-trained t2v backbone. Instead, it only replaces the continuous RoPE in current t2v models with shot-aware ones, i.e., Temporal-Cut RoPE (TcRoPE) and Temporal-Align RoPE (TaRoPE), without introducing new learnable parameters. These designs enable native multi-shot training without additional computational overhead. \n\n* The introduction of PortraitGala (250k single-shot and 400k multi-shot clips) fills a significant void in multi-shot human-centric datasets. The authors promise to release the model and dataset, which may promotes reproducibility and community advancement. \n\n### Weaknesses\n* More video results should be provided. As a video generation work, video results are crucial for readers to evaluate the effectiveness of the proposed method. Unfortunately, the authors only provide the videos with simply 3 shots and the generation conditions are unknown (MT2V? PMT2V? or InfT2V?). I think if would facilitate better evaluation and comparison if the videos corresponding to Figure 6 and 7 are also provided and the generation setting are further clarified. \n\n* The proposed framework seems to only suit for the single-subject setting. However, multi-shot video generation is more useful for generating scenes with multi-character communications and interactions (e.g., an interview or a sport event). It is unclear how the proposed framework would be adapted to multi-character settings.", "was_revised": true, "final_justification": "Thank the authors for the detailed rebuttal. I am glad to see that the authors have establish an open-sourcing timeline and promised to release the evaluation set as well as the visual results. Please do not just publish a coming soon link. I would like to maintain my initial positive rating after reading the rebuttal and other reviews.", "rebuttal": "__We are so grateful that all the reviewers give our work positive initial ratings (4454)__, which is a tremendous encouragement for us. We particularly thank you for your insightful feedback and for highlighting the innovation of our work. For each of the questions, we provide a detailed clarification below.\n\n***\n\n## Q1: Video Results\n__All videos in our supplementary materials were generated under the MT2V setting__, which represents the core contribution of our work for multi-shot generation from textual descriptions. These videos are representative samples from our 100-prompt evaluation set, which is the primary basis for the quantitative and qualitative analysis in our paper. The prompts, featuring 2 to 4 shots, were generated by LLM (Gemini-2.5 Pro) to ensure diversity and reduce bias.\n\nWe would like to __provide further details about the inference of two extensions__. Throughout the inference process, the resolution is 480p and the total length is 8 seconds, the same as MT2V. The PMT2V and InfT2V models are tuned based on the MT2V model. For PMT2V inference of Figure 6, given an input image, we first instruct Gemini-2.5 Pro to describe the ethnicity, gender, age group of the person and generate multi-shot prompts based on the attributes. We then randomly decide a segmentation of shot lengths and crop the face region with a face detection model. Finally, we input the face crop, the shot-length segmentation and multi-shot prompts to PMT2V model to generate the personalized multi-shot video. For InfT2V inference of Figure 7, we instruct Gemini-2.5 Pro to generate 10 prompts ($p_1$-$p_{10}$) featuring the same person and sample 10 random noises $z_1$-$z_{10}$. $p_1, z_1$ is treated as the reference. We first input $z_1, z_2$ and $p_1, p_2$ to generate shots $s_1$ and $s_2$, using the InfT2V model. Then, we iteratively input $z_1, z_k$ and $p_1, p_k$ ($k\\in \\[3, 10\\]$) to generate the other eight shots.\n\nUnfortunately, the rebuttal policy prevents us from supplying additional video files of Figures 6 and 7. Instead, we __provide partial prompts used for Figures 6 and 7 (please see postscript)__. Besides, to enhance reproducibility and contribute to the community, __we promise to publicly release our full evaluation set, the detailed evaluation methodology, and the visual results from EchoShot and all baseline models__. We have established a clear open-sourcing timeline. In fact, to facilitate early access, we have already publicly released codes and weights. We believe this will significantly strengthen our work and be a valuable resource for future research.\n\n## Q2: Extension to Multi-Character\nThis is an excellent question that we have also discussed ourselves. Rethinking EchoShot, it is __inherently adaptable to general multi-shot scenarios including single character, multiple characters and even objects__. The consistency across shots can be captured by introducing specific semantic patterns as indicators. Then EchoShot can automatically learn the correspondence between these semantics and the generated visual content. For your consideration, here we propose one potential solution for the multi-character scenario: __annotate each character in text prompts with a unique identifier__. For instance:\n\n> [Shot 1] (PERSON 1: a man with black hair) is running in a park …\n\n> [Shot 2] (PERSON 1: a man with black hair) is walking on the beach with (PERSON 2: a young boy wearing a blue shirt) ...\n\n> [Shot 3] (PERSON 2: a young boy wearing a blue shirt) is talking to (PERSON 3: a woman in a white dress) ...\n\nThen EchoShot could be trained directly on a dataset structured this way. We consider this a very promising direction for future work.\n\n## Q3: Typos\nWe will perform a thorough proofread of the entire manuscript to correct these and any other errors.\n\n***\n\nThanks again for your efforts. We believe the rebuttal stage is highly meaningful, and we will add the rebuttal discussion to the new version of our paper as much as possible. We would be greatly grateful if you would consider raising your score in light of these responses.\n\n***\n\n### Postscript 1: Prompts of Figure 6\n\nMorgan Freeman\n> [Shot 1] This segment shows a black, male, elderly person. The character's hairstyle is short, grey hair. The character is wearing a knitted cardigan, paired with a shirt and a tie. The character's expression is serious, with focused eyes, as if they are listening attentively to the phone. The corners of the character's mouth slightly downward, revealing a contemplative emotion. The character's movements are sitting on a beige patterned sofa, holding a white vintage telephone receiver in their right hand and holding an open notebook in their left hand. The video scene is a luxuriously decorated room with dark brown wooden walls as the background. There is a table on the left with a white tablecloth. The video is lit in a low-key warm tone, with light mainly focused on the character's face and a relatively dark background, creating a mysterious and oppressive atmosphere.\n\n> [Shot 2] This segment shows a black, male, elderly person. The character's hairstyle is short, grey hair. The character is wearing a knitted cardigan, paired with a shirt and a tie. The character’s expression is relaxed and cheerful. The character's movement is sitting on a rocking chair, with their left hand gently resting on the armrest. The video scene is a sunlit porch with lush green grass and distant mountains in the background, creating a beautiful scenery. The video is illuminated with bright light, making the facial details and background of the characters clearly visible.\n\n> [Shot 3] This segment shows a black, male, elderly person. The character's hairstyle is short, grey hair. The character is wearing a knitted cardigan, paired with a shirt and a tie. The character’s expression is serene and calm, with a kind gaze and a gentle smile at the corner of their mouth, conveying a sense of tranquility and wisdom. The character's action is resting on a bench in the park, with his eyes closed and listening to the sounds around them. The video scene is a vibrant park with dense trees in the background. The video is illuminated with bright natural light, evenly illuminating the characters and surrounding environment, creating a relaxed and pleasant atmosphere.\n\nEmma Watson\n> [Shot 1] This segment shows a Caucasian, female, young person. The character's hairstyle is shoulder length short hair, with a light golden color and a black thin headband. The hair is fluffy, slightly messy, and has a slight inward button at the end. The character is wearing a light blue shirt. The character's expression is happy and joyful. The character's movements are standing still, raising the head, looking up at the top of the screen and speaking. The video scene is a dark forest. There are tall trees in the background with mist and trees. The ground is covered with fallen leaves and some green plants. The overall lighting is dim, creating a gloomy and mysterious atmosphere. The light mainly comes from above, illuminating the face of the character.\n\n> [Shot 2] This segment shows a Caucasian, female, young person. The character's hairstyle is shoulder length short hair, with a light golden color and a black thin headband. The hair is fluffy, slightly messy, and has a slight inward button at the end. The character is wearing a deep red short sleeved dress and gold wire glasses. The collar of the dress is circular, and the sleeves reach the middle of the upper arm. The character's expression is slightly worried and uneasy. The character's movements are standing still and gazing downward. The video scene is a desolate place shrouded in mist. In the background, you can see some blurry mountain and forest. On the right side of the character, there is a tall stone pillar with a rough surface and some blurry patterns engraved on it. The lighting in the video is low-key, with overall dim lighting, creating a oppressive and mysterious atmosphere. The light mainly comes from above and in front, but the light is soft and there are no obvious shadows.\n\n> [Shot 3] This segment shows a Caucasian, female, young person. The character's hairstyle is shoulder length short hair, with a light golden color and a black thin headband. The hair is fluffy, slightly messy, and has a slight inward button at the end. The character is wearing a black V-neck dress. The character's expression is extremely sad and painful, with tears in their eyes, red eyes, and slightly parted lips. Their overall mood is low. The character's movements are standing still, with their head slightly raised, their eyes looking up, their lips slightly trembling. The video scene is indoors, with a light brown wall adorned with abstract tree patterns. The overall scene is simple and artistic. The video is illuminated with soft light, which is uniform and bright.\n\n### Postscript 2: Prompts of Figure 7\n\n> [Shot 1] This segment shows an Asian, female, and young person. The character's hairstyle is dark brown, with split hair and two thin braids tied at the back of the head. The ends of the braids are secured with black rubber bands. The character is a white high-neck shirt layered under a beige trench coat. The character’s expression is gentle and pleasant. The character is sitting and is in the process of drinking from a white ceramic coffee cup. The video scene is in a cafe, with a softly blurred background showing wooden furniture and green plants. The lighting is bright and soft, illuminating the subject evenly and creating a warm, inviting atmosphere.\n\n> [Shot 2] This segment shows an Asian, female, and young person. The character's hairstyle is dark brown, with split hair and two thin braids tied at the back of the head. The ends of the braids are secured with black rubber bands. The character is wearing a white t-shirt and a silver chain necklace, layered under a blue denim jacket ...\n\n（Omitted due to text length limitations）"}]

Author rebuttal:

Meta-review: This paper receives positive scores. The reviewers raise some concerns regarding the limited baseline breadth, the lack of evaluation on public benchmarks, the lack of model ablation studies, the model generalization ability to videos with non-photorealistic characters, etc. Most of the questions are addressed in the rebuttal and recognized by reviewers, with minor concerns in the issues of video temporal consistency. Authors should incorporate these review feedbacks, and release the code and demo videos in final version. Eventually, this paper is recommended to be accepted.
Decision: Accept (poster)