Title: EchoShot: Multi-Shot Portrait Video Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Portrait Video Generation

Multi-shot Video Modeling

Rotary Position Embedding

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

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 .

1 Introduction

Recent advancements in Diffusion Transformer-based (DiT) model [34] have catalyzed transformative progress in text-to-video (T2V) generation, enabling the synthesis of visually realistic single-shot videos [10, 6, 4, 7, 3, 43]. In real-world content creation workflows, users frequently demand multishot video generation with persistent subject consistency, particularly in human-centric applications such as 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 to 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 to perform this task through two

This work was completed during the internship at Alibaba Cloud Computing.

[CAMERA] [ATTRIBUTE] [HAIRSTYLE] [APPAREL] [EXPRESSION]

[BEHAVIOR]

stand still and talk

[BACKGROUND]

a blur lighting building

Consistent:

Shot 1:

[BEHAVIOR]

sit by the window, holding a cup of coffee, gently tasting it

[BACKGROUND]

in a cozy and romantic café

Shot 2:

Shot 3:

[BEHAVIOR]

take a leisurely walk, gaze into the distance

[BACKGROUND]

on a vast beach, seagulls flying

[CAMERA] [ATTRIBUTE][HAIRSTYLE]

[EXPRESSION] [BEHAVIOR] [BACKGROUND]

Consistent:

Shot 1:

[APPAREL]

a white shirt layered

with a black vest

[APPAREL]

a blue denim jacket,

a striped shirt

Shot 2:

[APPAREL]

a red sweater with

white star patterns

Shot 3:

Shot 2:

Shot 3:

Shot 1:

[HAIRSTYLE]

light brown, curly,

medium-length hair

[HAIRSTYLE]

silver, slicked-back

combed hair

[HAIRSTYLE]

red mohawk,

shaved sides

[ATTRIBUTE]

[HAIRSTYLE] [APPAREL]

[CAMERA]

arc-shot, close-shot

[BEHAVIOR]

hand raised, index finger pointing forward

[BACKGROUND]

a small shop counter with various goods

Consistent:

Shot 1:

[CAMERA]

zoom out from close-shot to middle-shot

[BEHAVIOR]

stand still and smile, blink

[BACKGROUND]

low trees and hills visible in the distance

Shot 2:

Shot 3:

[CAMERA]

zoom in from full-shot to middle-shot

[BEHAVIOR]

hold a phone in both hands, fingers swiping

[BACKGROUND]

a bedroom, pink wall, photos, posters

[CAMERA] [ATTRIBUTE]

[HAIRSTYLE]

[APPAREL]

a dark blue blazer, paired with a light-colored shirt

[EXPRESSION]

smile, with soft eyes conveying friendliness and ease

[BEHAVIOR]

raise her left hand then right hand to adjust her hair

[BACKGROUND]

white walls with black-and-white photographs

Consistent:

Shot 1:

Shot 2:

Consistent:

[CAMERA] [ATTRIBUTE][APPAREL]

[EXPRESSION] [BEHAVIOR] [BACKGROUND]

[APPAREL]

a white base sleepwear outfit with pink vertical stripes

[EXPRESSION]

furrowed brows, downturned lips, sadness and sorrow

[BEHAVIOR]

lean against headboard then cover chest with right hand

[BACKGROUND]

a dark wooden headboard, red dotted wallpaper

X

品

X

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 to adversarial competition between identity preservation 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-video 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-video (I2V) models [4, 3, 43]. Although this decoupled strategy partially mitigates the multi-shot consistency challenge, it fundamentally suffers from the bottleneck effect and highly depends on the performance of CT2I 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 a 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 domain-specific post-training strategies can enable the DiT architecture to intrinsically model appearance-motion consistency across multi-shot videos. To this end, we introduce EchoShot, an advanced multi-shot portrait video generation framework built upon the stateof-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

clips and 400k many-to-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 Temporal-Align 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-based personalized multi-shot video generation (PMT2V), and designing a disentangled RefAttn mechanism for infinite shots video generation (InfT2V). These extensions highlight our model'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-shot 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 a 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 as Open-Sora-Plan [27], CogVideoX [51], HunyuanVideo [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 [34, 15], the evolution from spatio-temporal [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, 24]. Despite advancements, existing methods are limited to single-shot videos, and extending pretrained T2V models for multi-shot scenarios poses a critical challenge.

Consistent generation . Maintaining subject consistency across scenes has broad real-world applications, with early efforts focusing on 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 alignment. With advances in video generation, these approaches could be applied to keyframe generation, combined with I2V 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 identity-preserving embeddings 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-event and multi-shot generation. HunyuanVideo [24] supports two-shot generation through caption concatenation. 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.

Human video datasets . Diverse, large-scale human-centric video datasets are crucial for advancing 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, OpenHumanVid [25] introduces a large-scale and visually realistic dataset sourced from films, TV series, and documentaries for single-shot video pretraining/fine-tuning. 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-consistent portrait video datasets.

VAE

Encoder

VAE

Encoder

VAE

Encoder

VAE

Encoder

Concat & Add noise & Patch embedding

Shot 0

Shot 1

Shot 2

Shot 3

DiT Block

TcRoPE

Cross-Attention

Latent Match RoPE

TaRoPE

(a) DreamSh framework

Caption 3

Caption 2

Caption 1

Caption 0

[Camera][Attribute]

[Hairstyle][Apparel]

[Expression][Behavior]

[Background][Lighting]

······

Text

Encoder

TaRoPE

Norm & Project & Unpatchify & Split

(b) TcRoPE visualization

(c) TaRoPE visualization

Shot 0

Shot 1

Shot 2

Shot 3

···

Time

Self-Attention

Time

Shot 0

Shot 1

Shot 2

Shot 3

···

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 a video segment x during training, prevailing T2V models first encode it into a latent feature: z = E ( x ) , where E is a pretrained video encoder. The latent feature is then mixed with Gaussian noise ϵ , becoming a noisy sample z τ . The training is driven by a denoising process of rectified flow (RF) formulation [29, 15]: L = E ϵ τ ,τ, z || ( ϵ -z ) -u ϕ ( z τ , τ, c ) || 2 , where τ ∈ [0 , 1] is the denoising timestep, u ϕ ( · ) is a denoising network and c is the embeddings of the textual description. A typical implementation of u ϕ ( · ) is a DiT, combining self-attention and cross-attention layers. In the self-attention layers, given a 3D-position-indexed query or key vector f sa t,h,w ∈ R d , 3D Rotary Position Embedding (RoPE) [15] adds dimension-wise position information to it, forming ˜ f sa t,h,w :

where R d Θ 3D ,t,h,w represents a 3D rotation matrix determined by the positional index and a set of base angles Θ 3D . Details are provided in the Appendix. Notably, RoPE is generally excluded from crossattention 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 on 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 a multi-shot video paired with corresponding frame counts and textual descriptions for each shot: { x s , n s , p s } S -1 s =0 , where S denotes the total number of shots. Here, n s represents the frame count for the s -th shot, and p s specifies its textual prompt. During inference, users are empowered to define the desired frame counts and prompts for S ′ shots: { n s , p s } S ′ -1 s =0 , enabling customized multi-shot video generation with consistent identity. This task presents three key challenges compared to single-shot video generation:

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-Driven 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 TcRoPE. Starting with S training video segments, a video encoder is first utilized to separately encode them into compressed latent features z s , 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 a sa t,h,w,s at time t , height h , width w , and shot s , the processed feature can be denoted as:

t,h,w,s t,h,w,s t,h,w,s (2)

where j is the phase shift scale. Intuitively, TcRoPE 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.

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 a single comprehensive caption. However, this method imposes limitations on the length of each shot-wise 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, we 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:

where k is a hyperparameter that controls the mismatch suppression scale. Following feature modulation, attention calculation is performed, and the attention score between the query of s 1 -th shot and the key of s 2 -th shot is given by:

where A s 1 ,s 2 represents the standard vanilla attention, and δ ( · ) is a monotonically decreasing function within a specific input interval, with f (0) = 1 . Detailed derivation is provided in the Appendix. Consequently, the attention between the matched query and key (i.e., when s 1 = s 2 ) remains identical to that in 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 TcRoPE 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

(a) PMT2V pipeline

+

Shots

Cross-Attention

Self-Attention

TcRoPE

TaRoPE

Face

Text

Enc

Face

Enc

Captions

Concat

+

TaRoPE

Shots

TcRoPE

TaRoPE

Captions

Cross-RefAttn

···

···

1) During fine-tuning

Self-RefAttn

(b) InfT2V pipeline

2) During inference attempts

Noise 1

(ref)

Prompt 1

(ref)

Prompt 2

Noise 1

(ref)

Noise 3

Prompt 4

Prompt 1

(ref)

Prompt 3

TcRoPE

TaRoPE

RefAttn

RefAttn

···

TcRoPE

TaRoPE

RefAttn

RefAttn

Shot 2

Shot 1

Shot 3

Shot 4

Shot 1

(a) Caption case

(b) Word cloud of captions

(c) Distribution of shot-wise IDs

Number of shots

Number of IDs (log scale)

247,831

107,590

25,198

6,605

6,914

650,000 video clips

400,000 IDs

1,000 hours

This is a tilt-down middle-shot clip of a Caucasian, youth, female.

The

hairstyle

is short, golden-brown with the ends curled inward, with a side bang ...

The

apparel

is a dark gray, long-sleeve dress, ... , along with a necklace ...

The

expression

is slight concern, the facial muscles are somewhat tense ...

The

behavior

is walking down a staircase, with her right hand gripping the handrail ...

The

background

is a modern-style house interior, open staircase made of wood ...

The

lighting

is soft natural light, entering through the window, illuminating ...

sining

nose

person's

handsshadowsclose-shot

serene

caucasian

bright

atmospherefeaturing

right

gray

mouthabeblack

speaking

sense

sewsmooth

focns

wom

ID

acollar

middle-agemaleyouth

hrcolored

facediffused

stands

elderly

engaged

female

Caucasianskin

shirt

AH

Ncbfrop

Whitelooking

greeny

holdingShort.natural

korean

open

Mmfleck

bluehand

sits

arigl

spuce.

dark

wooden

subtle

eyesgaze

rieStyled

forclread"

Calmmviromment

Tm

standing

scene

neutral

thin

video segments corresponding to a user-specified image. To achieve this goal, as illustrated in Fig. 3(a), we incorporate a conditioning branch to encode the face input using a face encoder, which consists of ArcFace [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 is 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 a 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. 3, which computes two attention weight matrices:

where [ · ] is concatenation operation. Here, A 1 is employed to update the first shot, while A 2 is utilized to update the remaining shots. Integrating RefAttn, the model undergoes a quick fine-tuning to learn this new pattern. During inference, we 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.

3.4 PortraitGala: The First Multi-shot Portrait Video Dataset

Data processing. To support training, we meticulously construct a high-quality and large-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-person clips; 5) identity many-to-one clips showing the same person using in-house

EchoShot

[ATTRIBUTE]

Asian, female, youth

[HAIRSTYLE]

short, dark brown hair, slightly inward-curled ends

[CAMERA]

tilt-up, middle-shot

[EXPRESSION]

first focus and calm, then a little serious

[BEHAVIOR]

first sit at desk, then stand up and stare ahead

[BACKGROUND]

office desk cluttered with files, with a window

Consistent:

Shot 3:

[APPAREL]

dark blue shirt with blue ID badge, white T-shirt underneath

Shot 1:

Shot 2:

[CAMERA]

static, close-shot

[EXPRESSION]

painful and sorrow

[BEHAVIOR]

burst out cry

[BACKGROUND]

in pantry, water dispenser

[APPAREL]

white long-sleeve shirt, light blue skirt, canvas bag

[CAMERA]

hand-hold, middle-shot

[EXPRESSION]

happy, expectant and friendly

[BEHAVIOR]

open a brown door, body emerging from behind it

[BACKGROUND]

white walls with switch and controller

[APPAREL]

blue fine-striped blazer

[CAMERA]

static, close-shot

[EXPRESSION]

serious and focus

[BEHAVIOR]

hold phone and speak

[BACKGROUND]

inside a car

Shot 4:

inconsistent identity, not follow prompts

StoryDiffusion+Wan

inconsistent identity, dynamic drift, not follow prompts

HunyuanVideo

not support more than two shots

inconsistent identity, not follow prompts, cross-shot prompt intermingling

naturally support numerous shots, consistent details, highly align with shot-wise prompts, fine-grained controllability

dio

dip

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 8 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 a [CAMERA] of a [ATTRIBUTE].[HAIRSTYLE].[APPAREL].[EXPRESSION].[BEHAV IOR].[BACKGROUND].[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 on, which lays a solid foundation for our multi-shot training paradigm.

4 Experiments

Implementation details. EchoShot is implemented based on Wan-1.3B [43]. Throughout the three tasks, we set the resolution to 832 × 480 and use a 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 of 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.

Kling

ConsisID

inconsistent clothes, inconsistent artistic style, doesn't follow prompts

bias to reference, doesn't follow prompts, inconsistent identity

[EXPRESSION]

relaxed and cheerful

[BEHAVIOR]

sit in a rocking chair

[BACKGROUND]

on a sunlit porch, lush green grass behind

[HAIRSTYLE]

light golden hair with a black hairband

[HAIRSTYLE]

short, gray hair

[APPAREL]

wearing a cardigan and a tie

Consistent:

Shot 1:

[EXPRESSION]

serious and focused

[BEHAVIOR]

sit on sofa, hold a white vintage telephone

[BACKGROUND]

in a luxuriously decorated room

Shot 2:

[EXPRESSION]

serene and calm

[BEHAVIOR]

rest on a park bench, with his eyes closed

[BACKGROUND]

in a vibrant park, with dense trees

Shot 3:

Consistent:

Shot 1:

Shot 2:

Shot 3:

Face input:

Face input:

[APPAREL]

wear a light blue shirt

[EXPRESSION]

happy and joyful

[BEHAVIOR]

raise head slightly and is speaking

[BACKGROUND]

in a dark forest with mist

[APPAREL]

wear a red short-sleeved dress, with glasses

[EXPRESSION]

worried and uneasy

[BEHAVIOR]

stand still, gaze downward

[BACKGROUND]

in a dark forest with mist

[APPAREL]

wear a black dress

[EXPRESSION]

extremely sad and painful

[BEHAVIOR]

tears welling up, reddened eyelids

[BACKGROUND]

a brown wall adorned with tree patterns

inconsistent clothes, limited visual quality

bias to reference, doesn't follow prompts, limited visual quality

EchoShot

consistent appearance, vivid expressions, highly aligns with prompts, fine-grained controllability, good visual quality

Baselines. To evaluate the performance of EchoShot, we compare it with a variety of baselines. For MT2V, we include (1) keyframe-based pipelines, StoryDiffusion [56]+Wan-I2V [43] (SD+W) and IC-LoRA [20]+Wan-I2V [43] (IC+W), (2) HunyuanVideo [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 to 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 metrics from three different aspects: (1) Identity consistency. Following [53], we use FaceSim-Arc [14] and FaceSim-Cur [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 VLM to score in two dimensions. Static quality (Sta.) covers clarity, color saturation, content layout, etc. Dynamic quality (Dyn.) covers smoothness, temporal consistency, reasonableness, etc.

4.1 Qualitative Evaluation

MT2V. As shown in Fig. 5, limitations of baselines are observed: (1) The performance of the keyframe-based pipelines is constrained by keyframe priors. To specify, SD+W fails to follow the detailed prompts (e.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 HunyuanVideo, causes the intermingling of prompts of different shots and inferior controllability. For instance, in the videos generated by HunyuanVideo, 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-grained 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&InfT2V. 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 to 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. 1 shows the quantitative metrics of EchoShot and baselines in MT2V. For identity consistency, our method scores the highest 73.74% on FaceSim-Arc and 69.43% on FaceSim-Cur, achieving strong cross-shot identity consistency. In terms of prompt control, our method remarkably scores 95.84% 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 advantages and the effectiveness of our method.

User study. To present a human-aligned assessment of MT2V, we further conduct a user study involving 20 participants. They are asked to perform binary voting in 45 one-on-one matchups between our method and baselines in terms of

Identity consistencyPrompt controllability

Visual quality

0

20

40

60

80

100

Winning rate (%)

86.7

78.7

68.5

72.1

66.4

61.0

76.9

68.9

54.1

vs StoryDiffusion+Wan

vs IC-LoRA+Wan

vs HunyuanVideo

the above-mentioned 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.

[ATTRIBUTE]

Caucasian, male, middle-aged

[HAIRSTYLE]

Short, curly, deep brown hair, voluminous and messy

[CAMERA]

static, close-shot

[APPAREL]

light blue and white checkered shirt, collar open, round glasses

[EXPRESSION]

serious, slightly worried

[BEHAVIOR]

slight head movement, lips opening and closing while speaking

[BACKGROUND]

in an office, gray walls with windows, black cabinet with stacked files and folders

Consistent:

Shot 2:

Shot 1:

[CAMERA]

static, middle shot

[APPAREL]

light blue shirt, blue tie, white diagonal stripes, round glasses

[EXPRESSION]

focused eyes, slightly tired

[BEHAVIOR]

sit at a desk, right hand in a fist on the table

[BACKGROUND]

another office, venetian blinds, black phone, cabinet, files

SimpleConcat

4.6 s

3.2 s

no cut, prompts intermingling (apparel, background)

SimpleConcat + TcRoPE

Cut timestamp

prompts intermingling (apparel, background)

db

4.3 Ablation Study

To verify the effectiveness of each proposed mechanism, we carry out an ablation study. For rapid assessment, we build a reduced-scale 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) SC+TcRoPE+TaRoPE is our full method. As shown in Fig. 9, SC struggles to recognize the difference between shots, causing failure of shot transition. Though it 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 an 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.

5 Conclusion

We introduce EchoShot, a novel framework for multi-shot portrait generation that addresses the limitations of existing single-shot pipelines. EchoShot achieves high-quality, identity-consistent multi-shot video synthesis with flexible user-defined control over content and appearance through two shot-aware RoPE mechanisms: TcRoPE and TaRoPE. Furthermore, we develop PortraitGala, a largescale, high-fidelity human-centric video dataset designed to support cross-shot identity consistency and fine-grained controllability. Extensive experiments confirm that EchoShot surpasses existing methods in both quantitative metrics and qualitative assessments. Notably, EchoShot provides a promising solution to enhance artistic workflows in real-world applications.

Limitation. Our framework currently lacks the capability to directly extend shots from previous content, thereby limiting the generation of longer, continuous video segments within a single shot. Additionally, generating consistent multi-subject scripts is a promising direction for future work.

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EchoShot: Multi-Shot Portrait Video Generation

Appendix

Table of Contents

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 two shot-aware mechanisms, TcRoPE and TaRoPE. Here, we provide the complete denotation of RoPE, following [40].

1D-RoPE. The proposed TaRoPE is based on 1D-RoPE, which is intended for sequential modality along a single dimension. Given a position-indexed query q m ∈ R d or key vector k m ∈ R d , 1D-RoPE can be expressed as:

where R d Θ 1D ,m is the rotary matrix, denoted as:

with pre-defined parameters Θ 1D = { θ i = 10000 -2( i -1) d , i ∈ [1 , 2 , · · · , d 2 ] } . Thus, the proposed TaRoPE in Eq. (3) can be further written as:

3D-RoPE. The proposed TcRoPE is based on 3D-RoPE, which is an extension of 1D-RoPE tailored for video modality. Given a 3D-position-indexed q t,h,w ∈ R d or key vector k t,h,w ∈ R d , 3D-RoPE can be denoted as:

where R d Θ 3D ,t,h,w can be denoted as:

with pre-defined parameters Θ 3D = { θ i = 10000 -2( i -1) d , i ∈ [1 , 2 , · · · , d 6 ] } . Thus, the proposed TcRoPE in Eq. (2) can be further written as:

A.2 Proof of Claim about Attention Score with TaRoPE

In Eq. (4), we give a concise formulation of the attention score after TaRoPE with claims. We provide a detailed proof of the claim here. Given queries of the s 1 -th shot q s 1 and keys of the s 2 -th shot k s 2 , if we divide them into 2-component pairs along the channel dimension, the inner product of their TaRoPE-enhanced representations can be written as a complex number multiplication:

where k s 1 , [2 i :2 i +1] represents the 2 i th to (2 i +1) th components of k s 1 and k s 1 , 2 i represents the 2 i th component of k s 1 . Note that the scalar k without subscripts is the mismatch suppression scale. When q s 1 and k s 2 are from the same shot (i.e., s 1 = s 2 ), Eq. (16) can be simplified as:

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 ( s 1 -s 2 ) varies, we follow [40] to denote h i = q [2 i :2 i +1] k ∗ [2 i :2 i +1] , S j = ∑ j -1 i =0 e i ( s 1 -s 2 ) θ i , and let h d 2 = 0 , S 0 = 0 , we can rewrite Eq. (16) using Abel transformation:

We plot the graph of the value of f ( x ) = ∑ d 2 -1 i =0 | S i +1 | as x = k ( s 1 -s 2 ) varies in [0,50] in Fig. 10, which shows a monotonically decreasing function. By synthesizing the above derivations, we arrive at the conclusion of Eq. (4).

0

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9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

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f(x)

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 a 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 OpenVid-1M [33] and OpenHumanVid [25]; and 3) a selection of videos obtained from various websites, 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 relevance. 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] to 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 of 4 were discarded.

To ensure that each video clip in PortraitGala featured only one individual, we implemented a person counting procedure leveraging YOLOv11 [23] for person detection and tracking. Subsequently, person identities were assigned using an improved version of HDBSCAN [32], a density-based clustering algorithm, coupled with a proprietary facial embedding extraction technique. A remaining challenge was the identification and removal of near-duplicate video clips depicting the same individual performing similar actions within a consistent setting. To address this, we simply employed Non-Maximum Suppression based on the detected person bounding boxes, specifically for videos with matching person IDs. Through rigorous manual verification, we 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.

You are now going to describe the film for audience. The description should be as detailed, comprehensive, and logical as possible.

The output must be in English, around 600 words.

Do not use vague words such as "appear to be" or "seem." Your descriptions must be definitive, precise, and highly accurate.

There is only one main person, and all descriptions revolve around this person.

All sentences must be affirmative statements. No questions or negative sentences are allowed.

Do not determine the gender of the main character. Use "the person," "the person is," or "the person's" as pronouns.

Please provide a description of the entire video, rather than describing each individual image.

Each input requires descriptions of the following eight aspects, with each aspect not exceeding 100 words, and the key points of the descriptions between the aspects should not overlap:

(a) Hairstyle: Provide a detailed and comprehensive description of the hairstyle of the person. Start with 'The person's hair ...'.

(b) Expression: Provide a 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 ...'.

(c) Apparel: Provide a detailed and comprehensive description of the clothing worn by the person (including lower body garments if visible). If there are accessories (e.g., glasses, earrings, watches), describe them as well. Start with 'The person wears ...'.

(d) Behavior: 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 set design, provide a detailed and comprehensive 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 (15.9%), and full-shot (12.7%). In terms of gender distribution, male individuals are predominant at 56.7%, while female individuals constitute 39.7%, and others make up 3.6%. The age-group distribution shows a vast majority of youth individuals at 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 18.1% are other ethnicities. This statistic breakdown highlights the diversity present in the PortraitGala dataset.

Close-shot

33.6%

Middle-shot

37.8%

Full-shot

12.7%

Long-shot

15.9%

Shot-scale distribution

Male

56.7%

Female

39.7%

Other

3.6%

Gender distribution

Youth

74.9%

Middle-aged

22.6%

Other

2.5%

Age-group distribution

White people

51.2%

Asian people

11.4%

Black people

19.3%

Other

18.1%

Ethinicity distribution

B.3 Dataset Examples

To provide a clear and intuitive representation of the dataset, we present a gallery of multi-shot video instances in Fig. 12. It highlights the diversity of the video distribution and the granularity of the captions.

R

This is a zoom-out middle-shot clip of a Caucasian, youth, female.

The hairstyle is long, straight, and dark brown. It is parted centrally and cascades smoothly over both shoulders, extending past the chest.

The apparel is a light beige corduroy jacket with a collar, button front, and two flap chest pockets. Underneath, a light green t-shirt is visible, featuring a red trim around its crew neckline.

The expression is consistently neutral and composed. The gaze is direct towards the camera with steady, open eyes. The mouth remains closed, lips slightly pressed together, conveying a serious and focused demeanor throughout the clip.

The behavior is standing perfectly still and upright in the center of the frame. There are no discernible movements of the body or limbs. The person maintains an unwavering direct gaze at the camera, without any interaction with objects.

The background is a large, dark chalkboard densely covered with white chalk. It displays complex mathematical equations, scientific graphs like a normal distribution curve, and various geometric patterns. The setting signifies an academic or scientific environment.

The lighting is warm, golden light emanates from a dense array of exposed light bulbs positioned above. A direct key light illuminates the person's face from the front. The overall lighting is bright and focused on the person, with the background also visibly lit.

C More Experimental Details

All the training is carried out on NVIDIA A100 80GB 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:

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 our motivation as well as verify the effectiveness, we conduct an intuitive experiment between EchoShot and the vanilla model. To specify, we perform a three-shot portrait video generation with EchoShot given three cut 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 a 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, TcRoPE allows the model to flexibly allocate attention to potentially crucial pixels, whether from the same shot or from different shots. Fig. 13(d) confirms a similar conclusion to the above one, in the context of TaRoPE and cross-attention.

Shot 1

Shot 2

Shot 3

(c) Self-attention score with TcRoPE

Shot 1

Shot 2

Shot 3

Shot 1

Shot 2

Shot 3

Prompt 1

Prompt 2

Prompt 3

(d) Cross-attention score with TaRoPE

Shot 1

Shot 2

Shot 3

Shot 1

Shot 2

Shot 3

(a) Self-attention score without TcRoPE

Shot 1

Shot 2

Shot 3

Prompt 1

Prompt 2

Prompt 3

(b) Cross-attention score without TaRoPE

D.2 More Quantitative Results

In Tab. 1, we present the quantitative results. As a supplement, we propose the stand deviation results of these metrics across the test set here.

Method

StoryDiffusion+Wan-14B

IC-LoRA+Wan-14B

HunyuanVideo-13B (2 shots)

EchoShot-1B (ours)

Identity consistency

FaceSim-Arc

FaceSim-Cur

6.24

7.02

9.16

7.13

6.86

6.75

8.28

7.91

Prompt controllability

App.

Mot.

Bg.

9.50

10.16

10.13

6.51

22.47

18.41

18.35

11.25

21.29

19.77

17.95

6.68

Visual quality

Sta.

Dyn.

15.20

13.08

5.76

4.90

2.57

2.68

3.51

2.77

D.3 Quantitative Ablation Study

We further conduct a quantitative ablation study following Sec. 4.3 in Tab. 4. Benefiting from the curated high-quality multi-shot dataset, the identity consistency and visual quality of three ablation models exhibit minimal variation. In terms of prompt controllability, the full model demonstrates significant advantages over the comparing models, echoing the qualitative results. The ablation study confirms that TcRoPE 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 k in Eq. (3). To investigate the appropriate parameter selection, we conduct an intuitive parameter analysis with the reduced-scale dataset. We leverage the mathematical analysis and empirically pick several typical values, j ∈ { 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, a too small j excessively enhances the inter-shot visual interactions, leading to occasional cut failure and prompt intermingling with low prompt controllability scores. While a too large j nearly blocks the interactions, causing identity inconsistency with low identity consistency scores. Similarly, a too small k gives rise to prompt intermingling while a too large k results in a slightly decrease in scores. Consolidating the results, we choose j = 4 , k = 6 as the default setting.

E EchoShot Gallery

F 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 is to evaluate different methods for generating multi-shot portrait videos showing the same identity. Your feedback will help us 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 a series of one-on-one matchups. Each matchup will present results generated by our proposed method and a random baseline method (both anonymous). Your task is to vote for your preferred option in 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 or 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 or explanation. If you decide to withdraw, your responses will not be included in 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.

8. Questions or Concerns

If you have any questions about the study or 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 to 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 were provided with detailed information about the purpose of the research, and their rights as participants. Consent was obtained from all participants prior to 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 to 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 or personally identifiable information was collected during the study.

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, identityconsistent, and content-controllable portrait video creation, EchoShot democratizes access to advanced video production tools, empowering creators of all skill levels to produce professional-grade 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 displacement may occur for traditional video editors and artists who rely on conventional methods, necessitating reskilling and adaptation to remain competitive in an evolving job market. Ethical concerns 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 on unbiased datasets is essential to prevent the reinforcement of harmful stereotypes or biases in generated content. To address these issues, we inherit safeguards from foundational models Wan2.1, 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 AI development.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n* 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**.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nThe authors discussed limitations but not potential negative societal impacts.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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).\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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\n\nQUESTIONS:\nI 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nEchoShot 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\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}