FilMaster: Bridging Cinematic Principles and Generative AI for Automated Film Generation
Authors: Kaiyi Huang
Yukun Huang
Xintao Wang
Zinan Lin
Xuefei Ning
Pengfei Wan
Di ZHANG
Yu Wang
Xihui Liu
Venue: ICLR 2026 Poster (2026)
Keywords: video generation
filmmaking
Primary area: generative models

Abstract: Existing AI-based film generation systems can generate high-quality videos, but struggle to design expressive camera language and establish cinematic rhythm. This deficiency leads to templated visuals and unengaging narratives. To address these limitations, we introduce FilMaster, an end-to-end automated film generation system that integrates real-world cinematic principles to generate professional-grade, editable films. Inspired by professional filmmaking, FilMaster is built on two key cinematic principles: (1) camera language design by learning cinematography from extensive real-world film references, and (2) cinematic rhythm by emulating professional post-production workflows. For camera language, our Multi-shot Synergized Camera Language Design module introduces a novel scene-level Retrieval-Augmented Generation (RAG) framework. Unlike shot-level RAG which retrieves references independently and often leads to visual incoherence, our approach treats an entire scene, comprising multiple shots with a shared spatio-temporal context and narrative objective, as a single, unified query. This holistic query retrieves a consistent set of semantically similar shots with cinematic techniques from a large corpus of 440,000 real film clips. These references then guide an LLM to synergistically plan coherent and expressive camera language for all shots within that scene. To achieve cinematic rhythm, our Audience-Aware Cinematic Rhythm Control module emulates professional post-production, featuring a Rough Cut assembly followed by a Fine Cut process that uses simulated audience feedback to optimize the integration of video and sound for cinematic rhythm. Extensive experiments show superior performance in camera language and cinematic rhythm, paving the way for generative AI in professional filmmaking.

Published as a conference paper at ICLR 2026
FILMASTER: BRIDGING CINEMATIC PRINCIPLES AND GENERATIVE AI FOR AUTOMATED FILM GENERATION
Kaiyi Huang' Yukun Huang'
Xintao Wangat
Zinan Lin
Xuefei Ning"
Pengfei Wan
Di Zhang? Yu Wang Kling Team, Kuaishou Technology
Xihui Liu!'
'The University of Hong Kong
3 Microsoft Research
*Tinnghua University
ABSTRACT
Existing Al-based film generation systems can generate high-quality videos, but struggle to design expressive camera language and establish cinematic rhythm. This deficiency leads to templated visuals and unengaging narratives. To ad- dress these limitations, we introduce FilMaster, an end-to-end automated film generation system that integrates real-world cinematic principles to generate professional-grade editable films. Inspired by professional filmmaking. FilMaster is built two key cinematic principles: (1) camera language design by learn- ing cinematography from extensive real-world film references, and (2) cinematic rhythm by emulating professional post-production workflows. For camera lan- guage, our Multi-shot Synergized Camera Language Design module introduces a novel scene-level Retrieval-Augmentcc Generation (RAG) framework. Unlike shot-level RAG which retrieves references independently and often leads to vi- sual incoherence, our approach treats an entire scene, comprising multiple shots with shared
context and narrative objective, as a single, uni- fied query. This holistic query retrieves consistent set of semantically similar shots with cinematic techniques from a large corpus of 440,000 real film clips. These references then guide an L.LM to synergistically plan coherent and expres- sive camera language for all shots within that scene. To achieve cinematic rhythm, our Audience-Aware Cinematic Rhythm Control module emulates professional produprod featuring Rough Cut assembly followed by Fine Cut process that uses simulated audience feedback to optimize the integration of video and sound for cinematic rhythm. Extensive experiments show superior performance in camera language and cinematic rhythm, paving the way for generative AI in professional filmmaking. Here is the Project Page
INTRODUCTION
Rapid advancements in generative AI (Kong et al., 2024; Yang et al., 2024) and (Multimodal) Large Language Models ((M)LLMs) (OpenAI, 2024; Team et al., 2023) have opened up new possibilities for automated film production (Zhang et al., 2025b). However, current automated film genera- tion systems often produce content that visually templated and rhythmically flat, falling short of professional cinematic standards. For instance, in terms of cinematography, methods like Anim- Director (Li et al., 2024), MovieAgent (Wu et al., 2025), LTX-Studio (LTX Studio, 2024) often gen- erate incoherent templated shots within scene (e.g., "static shot" in Figure 1), as their planning relics on LLM imagination without professional filmmaking knowledge. Furthermore, regarding cinematic rhythm, simply concatenating video clips with limited or desynchronized audio create a final film (Wu et al., 2025; LTX Studio, 2024) often results in a flat and unengaging experience for the audience (Table 1). These limitations reveal fundamental gap: current automated Al systems struggle to understand and implement the core cinematic principles (Bordwell et al., 2004) that are crucial for crafting effective narratives (Rabiger, 2013).
"ork done during an internship Kling Team, Kuaishou Tech. Corresponding author.

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Published as a conference paper at ICLR 2026
Ard-- Desporr
LTX-Suude
Figure 1: Qualitative comparison film generation in the case "little prince". Ours show more expressive camera language, compared to Anim-Directon MovieAgent, and I.TX-Studio. More details are in Section 4.3.
Inpul: text, reforence images
FilMaster: learn and emulate cinematic principles
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Figure 2: Video samples generated by FilMaster. Using the input of and reference images for characters and locations, FilMaster crafts high-quality films complete with professional camera language and cinematic rhythm, including rich, multi-la layer-d audiovisual outputs (folcy, sound effects (SFX), voice-over(VO), back- ground ambiance, musical scoring, and video).
To address these challenges, we propose FilMaster, an end-to-end automated film generation system designed to bridge the gap from script screen, shown in Figure 2. Inspired by professional filmmaking, our approach is built upon two key cinematic principles (Bordwell et al., 2004): (1) Camera language design by learning cinematography from extensive real-world film references. Filmmakers traditionally hone camera language skills to improve narrative coherence and expres- siveness by studying extensive film references. Drawing inspiration from this practice. FilMaster learns from a vast corpus of real film clips to apply professional camera language. (2) Cinematic rhythm control by emulating professional post-production workflows. FilMaster emulates the post- production workflow to control narrative pacing, and construct immersive audiovisual experiences, which are crucial for engaging experience (Case, 2013). Guided by these principles, FilMaster's two core modules (Multi-shot Synergized Camera Language Design and Audience-Aware Cine- matic Rhythm Control) drive a two-stage workflow for generation and post-production, yielding fully editable and structured film (Figure 3).
Specifically, the innovation for camera language our Multi-shot Synergized Camera Language Design module, which generates coherent and expressive cinematography. Our approach is centered on novel scene-level Searieval-Augment limitations of simpler, shot-level RAG methods. A shot-level RAG approach treats each shot as an independent query, retrieving cinematic references in isolation. This often leads to visual incoher- ence, where camera movements and styles change erratically between consecutive shots within the same scene (Figure 5). In contrast, our scene-level framework treats an entire scene, comprising multiple shots that share the same spatio-temporal context and narrative objective, fied query. This scene-level query is used to perform one retrieval operation from our database of 440,000 annotated film textual descriptions. The retrieved cinematic references then guide L.M
Generation (RAG) framework that moves beyond the
single, uni-
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Table 1: Comparison of film generation capabilities across different Al methods: Miim-Director Li al. (2024), MovieAgent Wu et al. (2025), and
(commercial product) LTX Studio (2024).
Camera
Script Design
#Audio Types
A/V Sync
Video Adj. (Structure)
Video Adj. (Duration)
Audience Review
Editable Output
Language
Method
Design
Anim-Director
0
X
X
X
MovieAgent
Templated
I
X
x
X
X
x
LTX-Studio
Templated
Limited
x
X
X
FilMaster (Ours)
(Fllm-rffereneed)
to synergistically re-plan the camera language for all shots within the scene. By enforcing a unified creative direction at the scene level, our method produces camera work that is both cinematically coherent and narratively expressive.
To achieve cinematic rhythm, we propose Audience-Aware Cinematic Rhythm Control mod- ule emulates professional, audience-aware post-production workflow. This process is driven by MILMSs prompted to adopt distinct professional roles (i.e., audience, film editor, sound designer): Rough Cut assembled by the generated video clips and then reviewed by an MLLM acting as a simulated audience. The MLLM can be conditioned
a specific demographic profile (e.g., "short-drama audience") to generate targeted feedback. This feedback then guides the Fine Cut pro- where MLLMs perform both video editing and sound design. Video editing involves structural and durational adjustments to video clips. Sound design ensures that a rich, multi-track soundscape crafted, integrating diverse audio elements (background ambiance, musical scoring, voice-overs (VO), foley, and sound effects (SFX)) with multi-scale audiovisual synchronization. Through this Audience-Aware process, FilMaster adjusts narrative pacing and enables effective integration of audiovisual elements, resulting in outputs with immersive and engaging rhythms.
To summarize, our contributions are as follows:
We propose FilMaster, end-to-end automated film generation system that operationalizes cinematic principles for high-level creative control. transforms the user input into a complete film, delivered editable and structured format compatible with professional production workflows.
We introduce novel Scene-Level, Multi-shot Synergized Camera Language Design for camera planning, that overcomes the limitations of static, templated or incoherent cin- ematography. By leveraging the -uugmeet-- Generation (RAG) approach with scene-level query a large corpus of real films, it produces coherent and expressive cam- era language for each scene.
We propose a Audience-Awer Aware Cinematic Rhythm Control for cinematic rhythm. This mechanism simulates audience feedbacks to guide automated video editing and multi-scale sound design, resolving issues of flat pacing and disjointed audiovisuals.
Experiments demonstrate that, compared existing open-source and commercial film gen- eration systems, our method achieves an improvement of 74.1 74.17% in camera language and 79. 79.26% in cinematic rhythm, based on human ratings.
2 RELATED WORK
2.1 VIDEO GENERATION
Existing video generation models can be roughly categorized into diffusion model-based (Ho al., 2022: Singer et al., 2022; Zhou et al., 2022; Khachatryan et al., 2023; Luo et al., 2023; Blattmann al., 2023; He al., 2022; Wang al., 2023; Yang al., 2024), and language model-based (Ville- et al., 2022; Chang et al., 2023; Kondratyuk et al., 2023; Yu et al., 2023a;b; Chang et al., 2022). Video diffusion models excel by progressively refining noisy inputs into clean video samples, with recent advancements like Sora (Brooks et al., 2024), Hunyuan Video (Kong et al., 2024), and Wan- Video (Wang et al., 2025) demonstrating remarkably high-quality visual synthesis via sophisticated latent diffusion techniques. Video language models, such as VideoPoet (Kondratyuk et al., 2023), are typically derived from the family of transformer-basee language models that can flexibly incor- porate multiple tasks in pretraining, and show zero-shot capabilities. Recent works (Guo et al., 2025;
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Xiao et al., 2025; Cai al., 2025), such Ve03 (Google DeepMind, 2025), RunwayML (Runway AI, 2025), and MovieGen (Polyak et al., 2024) enable high-quality video generation. However, they require significant manual intervention for prompting post-production for film with audiovisual elements. In contrast, our work provides a fully automatic, script-to-screen film generation system.
2.2 VIRTUAL CINEMATOGRAPHY
Virtual cinematography aims incorporate cinematographic principles into both virtual and live- action videos (Christianson et al., 1996; Kardan & Casanova, 2008). The Virtual Cinematographer (VC) (He al., 2023) introduces paradigm for automatically generating complete camera specifi- cations in real time for capturing events in 3D virtual environments. Other works focus on planning suitable camera placements for interactive tasks (Gleicher Witkin, 1992; Mackinlay et al., 1990; Phillips al., 1992). For example, Drucker et al. (Drucker et al., 1992; Drucker & Zeltzer, 1995; Drucker, 1994) formulate camera control as a constrained optimization problem to determine opti- mal camera positions for different shot types. While prior approaches primarily emphasize explicit camera placement strategies, our method instead learns and applies cinematographic principles from film data and post-production workflows, leveraging recent advances in video generation models and multimodal large language models (MLLMs).
2.3
LLMS FOR FILM PRODUCTION
Recent works for preliminary exploration in film production have begun to leverage the emerging reasoning and planning capabilities of LLMs (Wei et al., 2022; Lin et al.. 2024; Zhang et al., 2025a). Anim-Director (Li et al., 2024) automates animation generation by employing large multimodal models (LMMs) to expand user inputs into coherent storylines, generate scene images and videos by iteratively optimization. The development of multi-agent systems (Park et al., 2023; Sun et al., 2024; Wu et al., 2023; Hong al., 2024; Yuan et al., 2024; Huang et al., 2024) has further spurred innovation. For example, FilmAgent (Xu et al., 2025) proposes multi-agent collaboration through iterative feedback and revisions in constructed 3D virtual spaces. MovieAgent (Wu al., 2025) multi-agent Chain of Thought planning for movie generation, which automatically structures scenes, camera settings, and cinematography. Distinct from these prior efforts, FilMaster significantly ex- pands the role of LLMs to orchestrate a more comprehensive end-to-end film production pipeline, from pre-production through post- pro-urroduttion. Crucially, our system uniquely integrates the learning of camera language from real film footage and the emulation of professional post-pree post-production work- flows for cinematic rhythm control, aspects not holistically addressed by previous L.LM-based film generation systems (Table 1).
3 METHOD
We introduce the overview of our system in Section 3.1, and detail the two core modules, Multi- shot Synergized Camera Language Design module in Section 3.2 and Audience-Aware Cinematic Rhythm Control module in Section 3.3, respectively. The pipeline is shown in Figure 3.
3.1 OVERVIEW OF FILMASTER
FilMaster is an end-to-end automated system for generating films. It takes a text and sets of refer- ence images for characters and locations input, and produces complete film, delivered as fully editable, multi-track timeline in the industry-standard format OpenTimelineIO (OTIO) (OpenTime- linelO, 2023). As illustrated in Figure 3, our framework operates in two-stage process: Generation Stage and Coordination Stage.
Let T denote the input text, Le the input set of character reference images, and 1 the input set of location reference images. The overall process is a composition of two stages, Generation Stage (g) and Coordination Stage (C). First, raw video clips generated:
Valips = GTT, It)
These clips are then processed in post- post-prrouction into the final film, Fulm:
Frlm C(Velis)
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Firelctt
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Filma Computt the with Eciting Sottoare jegg- Dollact Benahel
Figure 3: Overview of the FilMaster framework for end-to-end automated film generation. It processes user input to produce editable, structured outputs, guided by cinematic principles. FilMaster include the (1) Multi-shot Synergized Camera Language Design module within the Generation Stage, leveraging real film
data for coherent and expressive visuals, and the (2) Audience-Aware Cinematic Rhythm Control module within the Coordination Stage, emulating professional post-production for enhanced audience engagement. Examples shown in gray area.
where Film is the final film represented as an OTIO timeline, comprising both the video track and the audio tracks.
Generation Stage: From Input to Raw Footage. This stage translates the inputs (T, Les 41) into collection of raw video clips, Veliis = (V....., Vn It includes:
Script Elaboration: LLMs first perform coarse-to-fing refinement on the input text T, ex- panding it into n detailed scenes with rich spatio-temporal contexts (detailed in Section A).
Camera Language Design: The Multi-shot Synergized Camera Language Design module (see Section 3 3.2) then plans the camera language for each scene, referencing large corpus of real film clips to ensure visual coherence and narrative expressiveness.
Video Generation: A video generation model synthesizes the clips based on the planned camera language and the reference images L. and 4 (see Section B).
From Raw Footage to Final Film. This stage assembles the generated clips Valps into a polished film Falm by video editing and sound design based on audience-aware feedback (see Section 3.3). This stage is driven by MLLMS prompted to adopt distinct professional roles.
Coordination Stage:
Audience- Review: An MLLM, simulating target audience, reviews an initial Rough Cut and generates structured feedback on its pacing, structure, and audio.
Video Editing: Guided by this feedback, an LLM acting editor refines the visual sequence into a Fine Cut by adjusting shot order and duration.
Sound Design: A multi-track soundscape (background ambiance, musical scoring, foley, voice-over, SFX) is composed by generation or retrieval and synchronized with the video to create cohesive audio-visual experience.
The final film is multi-track OTIO file, packaging both video and audio and ensuring seamless integration with professional post-production workflows.
3.2 MULTI-SHOT SYNERGIZED CAMERA LANGUAGE DESIGN
To enable coherent and expressive camera language in the Generation Stage, we propose the Multi- shot Synergized Camera Language Design module that introduces a novel scene-level Retrieval- Augmented Generation (RAG) (Gao et al., 2023) framework. Different from shot-level RAG which causes incoherence by retrieving references independently for each shot (Figure 5), we propose a scene-level querying mechanism: multiple shots within the scene, which share the same spatio- temporal contexts and a unified narrative objective, are treated as a single query. This holistic query retrieves semantically similar shots from large-scale dataset of 440,000 annotated textual descrip- tions of real film clips, then the retrieved shots with cinematic techniques are used to guide an LLM to re-plan camera language for each shot within scene.
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A scene S, from Script Elaboration, is the primary unit of planning, comprising shot prompts P. its spatio-temporal context C' and narrative objective O (example shown in the upper right of Figure 3). Our reference dataset is collection of N film clips' annotated textual descriptions, Dam = (d1, d2....,dd ...... The entire process can be formalized as follows:
Peatio-Temporals Aware Indexing. This indexing prepares query vector of the scene and a vector database of film dataset. The scene S is encoded into a query vector using text embedding model E()):
B(S) = E((,,C,))
Concurrently, the entire film dataset of textual descriptions Dnim is split into small chunks, encoded into vectors { and stored in vector database Vab by applying the same embedding model to each textual description:
Vab {ou v; E(d,), d; E Dūm)
Film Reference Retrieval. Given the query vector we retrieve the top-K most relevant cinematic examples from Vab by finding the nearest neighbors the embedding space. This is achieved by identifying the indices I of the K vectors in Vab based on semantic similarity to q. The retrieved references R are the set of annotated textual descriptions corresponding to these indices:
= (d: E
Shot Re-planning. The LLM re-plans (Te plun) detailed shot prompts for the entire scene with camera language based on film references, conditioning on both the original scene description S and the retrieved references R. This is formulated as:
P Peamera The planp (S, R)
The output Peamera is coherent sequence of shot descriptions for the scene S, specifying shot types, camera movements, angles, and atmospheric characteristics. We conduct the above for scenes separately. This scene-level shot design ensures visual continuity and narrative impact, a key distinction from shot-level generation approaches.
3.3 AUDIENCE-AWARD CINEMATIC RHYTHM CONTROL
In the Coordination Stage, the Audience-Aware Cinematic Rhythm Control module orchestrates the film's post-production by emulating a professional workflow with a sequence of specialized (M)LLMs. This process transforms the set of raw clips Velips into final film Falm.
Audience-Aware Review. This process generates actionable feedback by simulating a target au- dience's response. Let Amrret be the specified audience archetype (e.g., "short-drama audience")). First, a Rough Cut Trough is assembled by concatenating the video clips Valips with placeholder audio descriptions. Next, an LLM leverages search tools to generate detailed audience profile Paudience based on the given archetype. An MILM, acting as simulated audience, then critiques the Trough conditioned on Paudiences producing set of critiques Feritique- Finally, these critiques are translated into structured set of actionable recommendations Ractions by another LLM, covering structural organization, timing and duration, and audio coherence.
Video Editing. Based on the audience-aware review Ractions this process refines the visual narrative achieve picture-locked Fine Cut, denoted Vinne Let Viough be the video track of T Tough- Guided by Ractions, the system applies a set of available editing operations O = (rearrange, trim, accelerate) the rough video track. The output Vane is a finalized visual sequence with optimized narrative pacing and structural flow.
Sound Design. The sound design process constructs rich, multi-track soundscape synchronized with the picture-locked video Vine- It employs multi-scale synchronization strategy. Audio tracks are then generated at their corresponding temporal scales based on Ractions: background ambiance and musical scoring for each scene, voice-overs for each shot, and foley and sound effects for each event. These individually generated tracks are then aggregated into a complete set of tracks, Anacks for the entire film. Finally, an automated mixing function produces the final audio track, Annal- The full implementation details each composition step are provided in Section and Section H. The final output Frlm is composed of Vine and A in OTIO format.

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Published as a conference paper at ICLR 2026
Table 2: Automatic evaluation of film generation on FilmEval of Camera Language (CI.) and Cinematic Rhythm (CRh). denotes commercial product. denotes that the same video model applied.
Metric
Anim-Director
Anim-Directon
MovieAgent
MovieAgent
LIX-Studio
Ours'
CL CRh 1
2.96
3.02
2.74
2.55
3.74
4.50
1.94
2.38
1.74
1.98
3.62
4.32
EXPERIMENTS
4.1 EXPERIMENT SETTING
Implementation Details. We use GPT-4o (OpenAI, 2024) for script generation, RAG, video edit- ing, and sound design (VO, background, musical scoring). For audience-aware review and sound design (foley and sound effect), we employ Gemini-2.0-Flan Elements 1.6 (Kling, 2025) as the video generation model, capable of incorporating multiple ref- erence images as conditions. The generated video clips are at 1920x resolution, comprising 153 frames per sequence. We use K = 3 for retrieval, text-embed-3-sman (OpenAI, 2025) for embedding model.
(Team et al., 2023). We use Kling
Evaluation Metries. To evaluate our method, employ both automatic evaluation metrics and user studies. We follow MovicAgent (Wu et al., 2025) to adopt VBench (Huang et al., 2023b) as evaluation metrics (see Section F.3). However, VBench focuses on the generated quality, lacking the ability to evaluate camera language and cinematic rhythm of the generated films. Given the absence of existing metrics tailored for this task, we establish FilmEval, holistic evaluation benchmark for both automatic evaluation and user studies. FilmEval is based on 6 high-level dimensions essential for assessing film quality: Narrative and Script (NS), Audiovisuals and Techniques (AT), Aesthetics and Expression (AE), Rhythm and Flow (RF), Emotional and Engagement (EE), and Overall Ex- perience (OE). These dimensions are further decomposed into 12 specific dimensions (e.g., Script Faithfulness, Narrative Coherence), and we use the combination of the twelve criteria to calculate Camera Language (CL) and Cinematic Rhythm (CRh) (detailed in Section E). For automatically evaluating the aspects, we use Gemini-1.5-Flass (Team et al., 2023) as the evaluation model de- signed to assess generated films across the defined dimensions. We validate the effectiveness of automatic evaluation by measuring its correlation with human judgments in Section F.4.
Test Dataset. Our evaluation employs a diverse set of 20 test cases comprising two distinct types: 10 cases from MoviePrompts (Wu et al., 2025), which feature detailed descriptions, with an average of 100.4 words; and 10 shorter, more concise prompts with an average of 15.2 words, specifically designed by annotators to evaluate our method's 's flexibility in handling varied input complexities.
Comparing Models. We compare our method against three automatic film generation systems: the animation generation method Anim-Director (Li et al., 2024), the movie generation agent MovieAgent (Wu et al., 2025), and the commercial platform LTX-Studio (LTX Studio, 2024). To ensure fair comparison, we include their video generation models with Kling 1.6, the same video model used in our method for the open-source basclines. For the commercial platform LTX-Studio, while we could not change its internal video model, we included its default automated sound effects (SFX) in our evaluation to assess its end-to-end performance.
4.2 QUANTITATIVE RESULTS
Automatic Evaluation. As shown in Table 2, our method achieves an average improvement of 67.46%, with significant gains in camera language (49.92%) and cinematic rhythm (85.15%). This represents a substantial lead over existing methods like Anim-Directe (+87.35%) and MovieAgent (+105.99%), which have deficiencies in camera planning and audiovisual coordination. We also out- perform the commercial system L.TX- (Tuu-Studio (+19.81 19.81% %), which struggles with narrative pacing and coherence. When using the same base video model (denoted by !), our approach excels. Basclines still underperform our method by 65. 15% and 94.49%, respectively. This is because: (i) The evalu- ation focuses on the overall quality including 12 dimensions instead of only visual quality; (ii) The baselines focus on the character consistency, but fail to effectively integrate the cinematic principles, which hinders the overall narrative coherence. Detailed results are available in Section F.I.
User Study. We conduct a user study to evaluate the quality of generated films. Five participants were instructed read the criteria defined in FilmEval, and were asked to rate each video indepen-
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Figure 4: The input and corresponding video frame extractions from our FilMaster, illustrated alongside asso- ciated multi-track audio. The detailed process of the video clip (1) is illustrated in gray area.
Table 3: User study on film generation methods. model (Kling 1.6) applied.
denotes commercial product.
denotes the same video
Method
NS 1
AT 1
AE f
RF 1
EE f
OE f
CL f
CRh
Avg f
Anim-Director
1.94
2.16
1.94
2.12
2.12
2.36
2.15
2.04
2.11
Anim-Director
1.94
2.35
1.44
1.94
1.84
2.20
2.16
1.85
1.95
MovieAgent
1.57
1.63
1.70
1.70
2.20
2.27
1.66
1.83
1.84
MovieAgent
1.32
2.38
1.68
2.02
1.96
1.92
2.01
1.89
1.88
LTX-Studin
2.28
3.04
3.22
2.90
3.16
2.96
2.80
3.05
2.92
Ours?
3.70
3.80
3.80
3.73
3.93
3.87
3.76
3.82
3.79
dently. We randomly selected five cases from our dataset, compared our FilMaster with the other five methods (including )). total, we collected 1,800 ratings, with 150 votes per evaluation crite- ria. We show the six dimensions in Table 3, and detailed results in Table 8. The results show that our FilMaster demonstrates superior performance in film generation compared to the baselines, with an average increase of 74.17% in camera language, and 79.26% in cinematic rhythm.
4.3 QUALITATIVE RESULTS AND COMPARISON
Example. As shown in Figure 4, our method generates descriptions with camera language and designs multi-track audio based on the text prompt derived from the input text, forming cohe- sive audiovisual narrative with camera language design and cinematic rhythm control. Additional examples are provided in Section F.6.
The baselines struggle to produce expressive cinematography, shown in Figure/ 1. Anim-Director and MovieAgent generate statie shots with fixed camera angles, failing to convey the narrative's progression. LTX-Studio, while visually appealing, also lacks deliberately designed camera movements support the story's objectives. In contrast, our method utilizes a dynamic and purposeful camera language. As shown in row 4 of Figure 1, the narrative expressiveness: In the first two images, the camera zooms in to close-up of the Little Prince, the White Fox, and the Rose to show the interaction. In the middle two images, it then shifts focus between character pairs to sequentially highlight their actions of delighting the Rose. In the last two images, it performs another close-up to emphasize the actions of the Little Prince giving a star to the Rose. (2) Cinematic Rhythm: Limitations in rhythm and audio design exist across the baselines. Anim-Directo lacks audio entirely, severely restricting its storytelling capabilities. MovieAgent only incorporates basic voice-overs, resulting in flat auditory experience. L.T--Studio employs black-box automated audio design with only audio type,
Comparison. (1) Camera Language:
camera actively participates
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Objective: Emphasize the insignificance of the Little Prince and the White Fox against the backdrop of the universe. Prompt Summary: Morning, asteroid, Little White standing quietly with wide eyes, twinkling.
Scene level
Shat-lewwl
Figure 5: Ablation study scene-level and shot-level RAG-based camera planning. Our scene-level approach (top) generates coherent and synergized sequences using coordinated movements like "zoom in", "closc-up", and "pull back". The shot-level approach (hottom) produces disjointed sequences with emattc movements ("orbit", "pan across"). failing maintain visual continuity.
Table 4: Ablation study on the effects of Camera Language Design Module and Cinematic Rhythm Control Module on overall performance (average across 12 criteria).
Method
w/o Camera Rhythm
w/o Rhythm
Ours
Avg 1
3.75
4.17
4.67
which often leads to desynchronization between visual events and sound effects, and its narrative pacing can be slow and repetitive. In contrast, our method leverages audience-aware feedback to guide both video editing and sound design. By utilizing multi-scale synchronization strategy, creates rich, coherent soundscape that is coupled with the visual pacing, resulting in more immersive and engaging cinematic rhythm.
4.4 ABLATION STUDY
We ablate our method without Multi-shot Synergized Camera Language Design module and Audience-Aware Cinematic Rhythm Control in Table 4 on the case of "Little Prince" with scenes and 16 shots. Quantitative results show that removing cinematic rhythm module significantly de- creases the average score in FilmEval, highlighting its role in cinematic expression with similar gen- erated content. We also examined the impact of Multi-shot Synergized Camera Language Design module, which helps to form a coherent generated content. We show the qualitative comparison of scene-level and shot-level RAG-based camera planning in Figure 5. Our scene-level generates coherent and synergized shots using coordinated movements like "zoom in", "close-up", and "pull back". The shot-level approach produces disjointed sequences with erratic movements ("orbit"" "pan across"), failing to maintain visual continuity. We show detailed ablation study of our method in Section F.5. including the effectiveness of scene-level shot re-planning and RAG, the impact of audience-awii aware components, the influence of dataset size, the robustness of RAG on different LLMs.
5 CONCLUSION
We introduced FilMaster, an end-to-end automated film generation system designed for professional-grate film generation. FilMaster uniquely integrates cinematic principles, focusing on camera language design and cinematic rhythm control, while ensuring industry-compatinn itable outputs. We propose Multi-shot Synergized Camera Language Design module that learns cinematography directly from a vast corpus of 440,000 real film clips. By designing a scene-level query mechanism and leveraging aetrieval-Augmentt pressive, coherent camera plans with narrative objective. Our Audience-Awand Cinematic Rhythm Control module emulates professional prot-production. This includes Rough Cut assembly, a Fine Cut process refined by simulated audience feedback, including video editing and sound design, all orchestrated to achieve compelling narrative flow and profound emotional impact. Furthermore, proposed FilmEval, comprehensive benchmark for assessing Al-generated films across six key cinematic dimensions. Extensive experiments demonstrate FilMaster's 's state-of-the -ftt------e-artt performance, with average improvement of 74.17% in camera language and 79. 79.26% in cinematic rhythm based on human ratings, showcasing significant advancements in generating films with expressive visual language and engaging rhythm.
ed-
Generation (RAG), this module produces ex-

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ETHICS STATEMENT
Our work utilizes an internal dataset of short clips derived from commercially available films for academic research, in accordance with fair use principles. To respect copyright, this dataset will not be publicly released; however, the pre- pre-processing of film data will be open-source.
We acknowledge two primary ethical risks: 1) The model may inherit societal biases present in the source films. 2) As generative tool, it has potential for misuse, such as fake news.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, provide the detailed descripitions of methodology in Section 3. For (M)LLM instructions used in our method, we provide in Section H. For the dataset used, we provide description of its construction, content, and statistics in Section G.
ACKNOWLEDGEMENTS
This work is supported in part by the NSFC-RGC Joint Research Scheme through the Research Grant Council of Hong Kong under grant N.HKU76925, and in part by National Nature Science Foundation of China (No. 62561 160156).
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A SCRIPT ELABORATION
Given an input text, the script is hierarchically expanded. This coarse-to-fine approach ensures coherent progression from abstract concepts to concrete visual descriptions that can be translated into audiovisual content. The script elaboration unfolds in four progressive steps, each building upon the output of the previous step, implemented through chain of specialized LLMs working sequentially. The LLM instructions and examples are shown in Section H.
Synopsis:
The input text is expanded into a synopsis that introduces theme, characters, and plots, forming the foundation for subsequent development. This step establishes the core narrative framework without detailed visual considerations.
Simplified Storyboard: The synopsis is enriched with background information, causal chains, narrative developments, and outcomes, ensuring the story has appropriate twists while maintaining narrative coherence and logical consistency. This step transforms narra- tive concepts into structured story events.
The simplified version is further elaborated into a detailed story- board containing concrete visual narratives. This includes both primary shots (featuring main character actions with specific descriptions) and contextual shots (showing surround- ing environments and establishing shots), alongside metaphorical imagery enhance emo- tional resonance and thematic depth. This step translates story events into visual sequences.
Detailed Storyboard:
Scene: The detailed storyboard is segmented into distinct scenes that function spatio- temporal contexts within one scene (i.e., events occurring in the same location during the same timeframe), based chronological order and spatial continuity. These scenes serve as fundamental units for subsequent audiovisual generation, ensuring consistent visual and narrative treatment across related shots. For each scene, we extract key elements includ- ing prompt, time, location, characters, visual elements, and narrative objectives (justifying each scene's narrative necessity for plot advancement or character development). These ex- tracted elements collectively provide structured input for the Scene-level Multi-shot Cam- era Design module. Here detailed audio descriptions (VO, background ambiance, musical scoring) for each scene are generated placeholder for audience-aware review (VO) and scene-level sound design (background ambiance, musical scoring).
B VIDEO GENERATION
In the Generation Stage, the video clips (Valps) are synthesized by video generation model, which is conditioned on three inputs: the textual prompts from the shot re-planning process, a set of character reference images (Iz), and set of location reference images (I).. To ensure the correct visual references are used for each scene, an MLLM selects the appropriate Ie and I/ from a user- provipro set based the scene's ' character and location descriptions (described in Section A). For flexibility, reference images are not provided by the user, they can be synthesized on-the-fly by an image generation model. All shots within the same scene are conditioned on the same reference images to maintain visual consistency.
C SOUND DESIGN
Directly relying MILMs to design multi-track audio (including background ambience, musical scoring, voice-over (VO), foley, and sound effects) often results in poor synchronization with video content. This is primarily due to the MLLM's limited capability in managing coordination across multiple audio layers and temporal alignment. To address this, we propose a multi-scale audio-visual synchronization strategy, which systematically designs different audio types at appropriate tempo- ral resolutions. This process is further supported by curated audio libraries or synthesized voice content, followed by systematic audio preprocessing.
generation (RAG) from
Specifically, synchronization is designed across three temporal scales:
Scene-level synchronization: Background ambience and musical scoring are assigned at the scene level to establish atmosphere and emotional tone. This builds upon the rough audio design from Generation Stage.
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Shot-level synchronization: VO, including narration and dialogue, is designed at the shot level to maintain close alignment with visual content and enhance narrative clarity.
Intra-shot synchronization: Foley and sound effects are assigned a fine-grained temporal resolution. MLLM analyzes video content with second-leve timecodes to detect specific events like actions, movements, and environmental elements that require sonic representa- tion, ensuring detailed audiovisual coordination.
To ensure audio quality and contextual relevance, we employ the RAG approach for non-voice audio tracks, similar to Multi-shot Synergized Camera Language Design. We construct curated audio library comprising 46,826 sound assets, including 5,877 music tracks and 40,949 other audio assets (atmospheric sounds, foley, and effects) sourced from the Internet. Each asset is tagged with semantic descriptors, emotional qualities, and acoustic properties to facilitate context-aware retrieval. For voice-over content, we utilize ElevenLabs' text-to-speech services (ElevenLabs, 2025), allowing for flexible and high-fidelity voice generation tailored to specific narrative demands.
Composing multiple audio tracks directly often leads to inconsistencies in loudness, frequency bal- ance, and dynamic range. To mitigate these issues, we apply audio preprocessing techniques that ensure cross-track and track-video harmonization. This includes:
Loudness units relative to full scale (LUFS) normalization to maintain consistent perceived loudness across tracks (e.g. -16 LUFS for voice content, -28 LUFS for background ele- ments).
Frequency adjustment to avoid spectral clashes between different types of audio, partic- ularly ensuring voice intelligibility by attenuating competing frequencies in background tracks.
Dynamic equalization enhance clarity and cohesion.
D ROBUSTNESS ANALYSIS OF FILMASTER
In this section, evaluate the robustness of our system. Specifically, boost the robustness of our system: 1) separating review of Rough Cut and video editing: 2) adding audience perspective help edit the videos; 3) disentangle multimodal information for (M)LLMs for processing information. We evaluate by ablating these components qualitatively and quantita- tively. We randomly choose 10 cases, including 160 shots test samples, and set breakpoints at the steps of structural organization, and timing and duration. We evaluate through two metrics: the corrected ratio and the success ratio. The corrected ratio is defined as whether they correct by com- paring with the original ones. The success ratio is defined as whether the correction is correct (there no contradictory responses, no factural fact). We also show qualitative comparison Figure and Figure 7.
three mechanism to
Separating review Rough Cut and video editing.
We observed that if we combine review and correction together into single MLLM, it will be overwhelmed with the task. The reasoning and analysis ability are therefore be impacted. Thus to keep the analysis, correction ability, we separate the review of Rough Cut and video editing. We found that by separating the clear task, the system becomes more robust and effective at correcting.
Table 5: Ablation study the robustness of system. We evaluate the Corrected Ratio and Success Ratio across 10 test cases.
Method
Corrected Ratio (%)
Success Ratio (%)
Analysis and Correct w/o Audience Perspective Ours (Full System)
60
50
50
50
100
100
Adding audience perspective to help edit the videos.
We observed that if we remove the perspective of audience, the post- protuuroouttion stage will be dom- inated by the perspective of director in the generation stage: There is correction of the Rough
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Cut, it will be considered good enough in the view of director. This director-only perspective hampers the effective correction, and cannot be adjusted to meet the requirements of different types of audiences.
Disentangling multimodal information for (M)LLMs for processing information.
Asking single MLLM to simultaneously process and reason about diverse multimodal inputs (video, audio, text) is a significant challenge, even for state-of-the-art models like Gemini. This complexity often leads ballucinations the neglect of specific requirements. To mitigate this, we break down the task by disentangling multimodal information. Instead of feeding all inputs into one model, approach first uses MILMM to generate detailed textual captions of the video. Then, separate LLM uses these captions, alongside the script and placeholder audio descriptions, perform the complex reasoning and editing (detailed in Figure 21-Figure 23). This separation of visual perception (captioning) from cognitive reasoning (editing) dramatically reduces complexity. As shown in Table 6, while a direct approach attempts all corrections, it fails to do successfully (0% success ratio). Our disentangled method, however, maintains high success ratio, proving its effectiveness for complex post-production tasks.
Table Ablation study on disentangling multimodal information. We evaluate effect Corrected and Success Ratios across 10 test cases.
Method
Corrected Ratio (%)
Success Ratio (%)
Directly addressing multimodal info Ours (Separating multimodal info)
100
100
90
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E FILMEVAL EVALUATION INSTRUCTIONS
FilmEval is based six high-level dimensions essential for assessing film quality: Narrative and Script (NS), Audiovisuals and Techniques (AT), Aesthetics and Expression (AE), Rhythm and Flow (RF), Emotional and Engagement (EE), and Overall Experience (OE). These dimensions are further decomposed into twelve specific criteria for detailed evaluation:
NS: Script Faithfulness (SF), Narrative Coherence (NC)
AT: Visual Quality (VQ), Character Consistency (CC), Physical Law Compliance (PLC), Voice/Audio Quality (V/AQ)
AE: Cinematic Techniques (CT), Audio-Visual Richness (AVR)
RF: Narrative Pacing (NP), Video-Audio Coordination (VAC)
EE: Compelling Degree (CD)
OF: Overall Quality (OQ)
While work highlights two key modules for camera language and cinematic rhythm, it's impor- lant to recognize that cinematic quality arises from the holistic integration of various elements. Our evaluation dimensions are therefore designed to capture only the direct outputs of each module but also their synergistic impact on the final film:
The Multi-shot Synergized Camera Language Design primarily evaluated through NS (SF, NC), ensuring visual storytelling aligns with the script, and through key visual aspects AT (VQ, CC, PLC), reflecting the quality and coherence the planned visual founda- tion. This module also lays the groundwork for effective AE (CT) by designing shots with inherent cinematic qualities and contributes to the visual component of AE (AVR).
The Audience-Aware Cinematic Rhythm Control module's effectiveness measured by audio-related aspects of AT (V/AQ), the realized AE (CT, AVR) through sophisticated edit- ing and sound design, the mastery of RF (NP, VAC), and the resulting EE (CD). This mod- ule coordinates the visual and auditory elements into cohesive and impactful rhythmic experience, evaluated by the ultimate arbiter OE (OQ).
Derived metrics (CL, CRh) are computed from preceding base metrics: CL is calculated as the average of (SF, NC, VQ, CC. PLC) 0.5 * average of (CT, AVR), CRh is calculated the average of (V/AQ, NP, VAC, CD, OQ) + 0.5 average of (CT, AVR).
To reduce subjectivity in evaluation,
set up the criteria with more observable metrics, such as "I point: Chaotic and disorderly - exhibits 2 major logical problems". We show the detailed criteria of FilmEval for both automatic evaluation and user study as follows:
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NARRATIVE AND SCRIPT
Script Faithfulness
point: Severely deviates from the original script - 2 50% of key scenes missing or replaced, two or more major character attributes altered, and three or more plot elements contradict the original.
50% of key scenes preserved, 1-2 major inconsistencies in character settings, and least two identifiable deviations from core plot events.
2 points:
Partially follows the original script <
points: Generally follows the original script - z 70% of key scenes preserved, character settings largely consistent with minor trait changes, deviations limited to nonessential details that do not affect the main storyline.
4 points: Highly faithful to the original script 2 90% of key scenes accurately repre- sented, all major character settings preserved, only minor omissions without plot contradic- tions.
points: Completely faithful to the original script - all key scenes, character settings, and relevant details correctly reproduced; no detectable deviations.
Narrative Coherence
Chaotic and disorderly exhibits 3 major logical problems (clear causal er- rors, contradictory temporal order, character actions contradict prior information, or plot discontinuities) that prevent coherent understanding.
point:
Basically understandable but flawed contains > 2 clear logical gaps missing transitions that noticeably disrupt narrative logic.
2 points:
3 points:
Generally coherent main storyline is clear; may contain one minor logical deficiency (e.g., underdeveloped motivation) that does not impede overall comprehension. Smooth and coherent flows naturally with reasonable plot development, clear cause-effect links, and only negligible logical issues.
4 points:
5 points:
Completely coherent natural, well-justified plot development with no logical holes; all cause-effect relationships are clear.
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Reordered
Structure after reordering and analysis:
1.Scene 1: Office Environment
1.Scene 1: The Routine Office Environment
1. 00:00-00:05
1. 00:00-00:05: A fluorescent light flickers above
2. 00:05-00:10
a computer screen. Sarah, young woman with turly hair, appears and begins to type. She wears tank and necklace.
2.Scene 2: Looking
Out the Window
2. 00:05-00:10: Close-up shot of Sarah at her desk, typing on the keyboard. Files and a desk lamp are seen on the desk front her.
1. 00:10-00: 00:10
2. 01:15-01:20
3. 00:15-00:20
4. 00:20-00:25
5. 00:25-00:30
2.Scene 2:
Longing for the Outside World
6. 00:30-00:35
1. 00:25-00:30: Close up of Sarah's side profile, looking out the window with necklace and small hoop earrings.
3.Scene Restaurant Interaction
00:20-00:25: A view the office from the corner. showing Sarah sitting at her desk looking towards the window.
1. 00:35-00:40
2. 00:40-00:45
3. 00:45-00:50
00:10-00:15 A shot Sarah in office from behind with bright light making everything blurry she looks the window.
4. 00:50-00:55
5. 00:50-01:00
4.Scene 4: Personal Reflection
1. 01:00-01:05
3.Scene 3: Daydreams of the City
2. 01:05-01:10
00:15-00:20: Close up of the city seen from
3. 01:10-01:15
Sarah's office. with her looking over buildings from high point.
4. 01:20-01:25
5. 01:25-01:26
2. 00:25-01:26. The view from where Sarah was stancing to the bright window with a sun flare.
(4) Combine review and correction together
4.Scene 4: Cafe Interaction
Scene 1: Introductionn and Initial Focus
1. 00:40-00:45 Medium shot of Sarah with curly hair and a man with gray hair and beard as they sit in a restaurant near a window. 00:45-00:50: Medium shot Sarah, the man, and third person to the right side of the shot. 3. 00:50-00:55 Close up of Sarah and the man at the restaurant table, with her speaking to the man.
Scene Office Introduction
100000 00:05 fluorescent light flickers above
computer screen. A young woman with curly hair appears and begins to type. She wears tank top and nocklace.
00:05-00:10 Close-up desk, lyping the keyboard. Files and desk lamp are scon on the dosk in front of her.
the woman her
00:55-01:00 Close up Sarah the the restaurant table, with her smiling and speaking to the man while gesturing with her hands.
Scene Captivated by the Window
00:10-00:15 A shot of the woman in her office from
gehind her. The bright light coming through the
window makes ovorything blurry and hard to sec.
5.Scene 5: Emotional Growth and Reflection 1. 00:30-00:35. Close up of Sarah's side profile, looking out the window with necklace and small hoop earrings.
She's looking put the window.
000105 2015000:200 Close up of city seen from the
woman's office, with her looking the buildings from high point.
01:00-01:05 Close up Sarah's is face upper torso, she flips through drawing callendar.
00:20-00:25 A view of the office from the corner, showing the woman sitting at her desk looking towards the window.
3. 01:05-01:10 Sarah turns and smiles at the camera. The next clip shows a woman carrying camera wearing red 01:20-01:25: Sarah standing and then look at the camera with a smile.
00:25-00:30 Close up of the woman's side profile, loaking out the window with necklace and small hoop earrings,
(b) Without audience perspective
(c) Ours (separate review and correction, w audience perspective)
Figure 6: Qualitative comparison of "Separating review of Rough Cut and video editing", and "Adding audience perspective". We illustrate the case of "Her gaze fixed that window" as example for struc- tural organization. For (a), combining review
correction together will lose the ability to make correction, directly copying the original order. For (b), without audience perspective, the system tend to keep the order designed by director in the generation stage. failing to make effective correction. For (c), ours show effective correction and logical explanation of the adjusted structure.
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[
Begin the script generation: Her gaze was fixed on that Response of duration: [
"shot "00:00-00.05"
"duration": '2s".
"mode":""re""
"shot 1": "00:00-00:05".
"reason": 'Start shorter to show office monotony without dwelling. Facus on light's harshness and her early senso of confinement."
"duration", 4
"mode": "accolorate",
"reason": 'The initial flickering light and pulling back
1.
shot sets the scene, but accelerating slightly maintains the stenile office feel without losing impact."
"shat 2": "00:05-00:10",
"duration": '3s".
"mode": "retain,
"shot 2": "00:05-00:10"
"reason" The change in lighting show different feel the atmosphere. the desk environment still depicts monotonousness.
"duration': 3.
"mode": "crap",
"reason": "Close-ue up of the typing scone should be
).
tightened to avoid monotony and emphasize
contrast lighting and work clutter."
"shot "00:10-00:15"
"duration": '2s'.
"mode": "crap".
"shot 3": "00:10-00:15".
"reason": "Reduce time spent here; emphasizes
"duration": 5.
Sarah's longing but avoid lingering too long on office setting. quick glance enough."
"mode" 'retain".
"reason": "Tiis scene introduces the personal touches and longing, so it should remain at full length to develop emotional depth."
).
"shot 4": "00:15-00:20",
"duration': '2s'.
"made": "crap"
shot 4" "00:15-00:20"
"resson": "Cut down the static clesk view
"duration':3 3.
maintain faster tempo. Focus on the window view."
"mode" 'crap'.
).
"reason": "The scene of office supplies posters shortened
previous information about the workspace"
"shot "00:20-00:25"
"duration": '3s',
"mode": "retain..
"reason": 'Allow more time to show the city view. emphasize how bustling enviranment outside contrasts Sarah's routine."
"shot 5": "00:20-00:25".
"duration': 4.
"mode": "accelerate",
}.
"reason": "Speeding the view of the city outside emphasizes her fixation on the warld outside the office without losing the shot's intent."
Directly addressing multimodal info
(b) Ours (Separating multimodal info)
Figure 7: Qualitative comparison of "Separating multimodal information". We illustrate the case of "Her gaze was fixed on that window" as an example for timing and duration. For (a), directly addressing multimodal information will let the model hallucinate with response, and fails to give propor correction reason. For instance, it tries to choose the retain mode that keeps the duration as original Ss, but give the instruction of reducing duration to 3s. For (b), separating multimodal information, the model can give self-consistent response without contradictory.
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AUDIO-VISUALS AND TECHNIQUES
Visual Quality
point: Severely broken visuals multiple critical failures (2 3) such as missing objects, heavy distortion, or broken frames; key elements unrecognizable.
points: Obvious visual flaws 2 scenes with missing or distorted elements; artifacts noticeably disrupt viewing.
points: Basically complete visuals - all essential elements present; occasional minor errors brief artifacts that do not affect comprehension.
points: Clear and complete visuals very few minor imperfections; no major missing or distorted elements.
points: Flawless visuals - all elements correctly rendered throughout; no observable distortions artifacts.
Character Consistency (visual)
Severely inconsistent designs major appearance attribute changes (e.g., face shape, hairstyle, clothing) across scenes; same character may appear as different individuals. Noticeable fluctuations character features change across multiple scenes; iden- tity recognizable but inconsistently portrayed.
point:
points:
points: Generally consistent appearance largely stable; occasional minor inconsisten- cies apparent only on close inspection.
points: Highly consistent stable features across nearly all scenes and angles; any in- consistencies are negligible.
points: Perfectly consistent - precise character features maintained in all scenes and actions; observable fluctuations.
Physical Law Compliance
point: Severely violates physics - extreme violations (impossible movements, wrong gravity, unrealistic collisions) that break realism.
2 points:
Multiple violations at least two clear physics errors causing noticeably unreal- istic motion or effects.
Generally compliant most motions follow physical expectations: mild artifi- ciality in some movements but acceptable.
3 points:
points: Good compliance natural movements and believable interactions; only minimal deviations.
points: Perfect compliance - movements, collisions, and effects behave in line with real-world physics; no anomalies.
Voice/Audio Quality
point: Extremely poor audio voiceovers unclear or missing; sound effeett chaotic or severely distorted; audio impairs comprehension.
2 points: Poor audio voiceovers occasionally unclear; sound effects minimal poorly synchronized; noticeably below standard.
3 point: Average audio voiceovers generally clear; sound effects appropriate but refined.
4 points: Good audio clear, well-mixed voiceovers and rich sound effects that effectively support scenes.
Excellent audio highly clear, expressive voiceovers and nuanced, well- synchronized sound design with no noticeable flaws.
5 points:
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AESTHETICS AND EXPRESSION
Cinematic Techniques
point: Single. stiff shots static, repetitive framing with no purposeful use of basic film language (composition, shot scale, angle).
points: Limited shot variation some shot types used but movements are stiff or poorly executed; film language inconsistent ineffective.
points: Common techniques used adequately - standard shots (close, medium, wide) and generally smooth camera movement; film language correct but not distinctive.
points: Rich film language - good variety of shots used intentionally; smooth, natural camera movements that enhance narrative emotion.
points: Highly creative and precise techniques - wide, inventive range of shots and precise camera execution; film language is expressive and purposeful.
Audio- Visual Richness
Extremely limited expression monotonous, repetitive visual/audio elements with minimal variation or layering.
1 point:
2 points:
Basic and formulaic attempts expression exist but are simple and predictable, with little stylistic diversity.
3 points:
Moderate diversity - some scenes show varied styles or tempos; overall richness uneven and lacking full coherence.
points: Visually and sonically expressive - multiple techniques used cohesively to create layered meaning, mood shifts, stylistic nuance.
points: Exceptionally rich audio-visure language - diverse, inventive, and highly ex- pressive use of sound and visuals that forms distinctive artistic voice and strong narra- tive/emotional impact.
RHYTHM AND FLOW
Narrative Pacing
Completely uncontrolled pacing multiple extreme pacing issues (2 3: abrupt skips, overly long pauses, rushed key events) that severely impair comprehension. least two clear pacing problems (e.g., rushed or overly elongated plot points) that disrupt rhythm.
1 point:
points: Obviously inconsistent pacing
points: Generally appropriate pacing reasonable progression with minor inconsisten- cies (one or two scenes slightly off) that do not hinder understanding.
points: Well-controleed pacing - natural scene durations and transitions; good balance between tension and relief.
points: Precisely controlled pacing - deliberate timing that enhances emotional impact and narrative clarity; smooth transitions between tempo changes.
Video-Audio Coordination
Severely unsynchronized persistent audio-video mismatches, repeated lip-sync errors of multiple frames, and frequent sound-action mismatches that disrupt viewing. Obvious lack of synchronization
point:
2 points:
recurring lip-sync or timing mismatches and poor coordination between voice and visuals.
3 points: Basically synchronized audio and video mostly aligned with occasional minor mismatches that do not interfere with viewing.
points: Good coordination voice, effects, and visuals well-aligned; synchronization errors are rare and minor.
points: Perfect synchronization all sound elements precisely match visual actions and lip movements; creates harmonious viewing experience.
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EMOTIONAL AND ENGAGEMENT
Compelling Degree
No appeal viewers cannot become immersed or emotionally connected; content fails to engage.
point:
Insufficient appeal weak emotional rendering; difficult to maintain viewer tention.
points:
points:
Basic appeal generates some interest but lacks depth for strong emotional
resonance.
Strong appeal - effective emotional rendering that elicits clear emotional re- sponses and sustained interest.
points:
Extremely compelling - powerful emotional tension and engagement that fully immerses viewers and produces strong resonance.
points:
OVERALL EXPERIENCE
Overall Quality
point:
Extremely poor overall quality - 2 3 core dimensions severely deficient; major
issues impair comprehension or viewing value.
2 points: Poor overall quality at least two major dimensions below acceptable standard; viewing value limited.
3 points: Average overall quality - most dimensions moderate acceptable; balanced
strengths and weaknesses; basic viewing value.
4 points:
Good overall quality majority of dimensions well-executed and cohesive; high viewing value with only minor issues.
5 points:
Excellent overall quality all major dimensions perform high level; consis- tent, precise, and artistic coordination yielding very high viewing and artistic value.
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Table 7: Automatic evaluation of film production generation across 12 evaluation criteria FilmEval, and 2 derived evaluation criteria for camera language (CL) and cinematic rhythm (CRh). denotes commercial product. denotes the same video model applicd. Blue represents the derived metric for CL., and green stands for CRh.
NS
AT
AE f
RF
EE
Derived
Method
Avg
SF
NC
VQ
CC
PLC
V/QQ
CT
AVR
NP
VAC
CD)
CI.
CRh
Anim-Directtrr
1.60
2.20
4.20
3.45
3.55
1.00
3.05
2.50
2.10
1.00
2.45
2.30
2.96
1.94
2.45
Anim-Directes
L.60
2,60
4.20
3,80
3,20
2.00
2,80
2.60
2.20
2.80
2.20
2.40
3.02
2.38
2.70
MovieAgent
1.50
1.60
4.10
3.40
3.40
2.70
1.60
1.00
2.20
2.20
2.74
1.74
2.24
MovieAgent
1.40
1,60
4.00
3.20
2.80
1.80
2.40
2.20
1.40
2.40
2.00
2.00
2.55
1.98
2.27
LTX "tudio
2.50
3,00
4.95
4.10
3.90
3.10
4.10
3.85
3.15
4.10
3.65
3.75
3.74
3.62
3.68
Ours'
3,90
4.60
5.00
5.00
4.40
3.80
4.10
4.10
4,40
5.00
4.20
4,40
4.50
4.32
4,41
F EXPERIMENTAL RESULTS
F.1 QUANTITATIVE RESULTS ON AUTOMATIC EVALUATION
We provide the detailed automatic evaluation on twelve dimensions in FilmEval based on Gemini- 1.5- 1---laah (Team et al., 2023). The results are shown in Table 7, with an average improvement of 67.46% in our FilMaster: 49.92% in camera language and 85.15% in cinematic rhythm, re- spectively. Our analysis reveals that existing approaches like Anim-Director (Li et al., 2024) and MovicAgent (Wu et al., 2025) significantly underperform across multiple dimensions including NS, AE, RF, EE, and OE These methods demonstrate particularly severe deficiencies in audio quality and video-audio coordination. In contrast, our proposed method achieves substantial improvements across all evaluation dimensions in FilmEval, with average performance increases of 87.35% and 105.99% compared to Anim-Director and MovieAgent, respectively. When compared with the commercial product LTX-Studio, observe that LTX-Studio struggles with script faithfulness, nar- rative coherence, narrative pacing, and audio quality, likely due to insufficient integration of camera language and audiovisual elements. Our approach outperforms LTX-Studio with an average im- provement of 19.81% demonstrating the effectiveness of our film generation system. As for the same video base model comparison (denoted as ²), the results show that the baselines underperform our approach by 65.15% and 94.49% because: (i) The evaluation focuses on the overall quality including 12 dimensions instead only visual quality; (ii) The baselines focus on the character consistency, but fail to effectively integrate the cinematic principles, which hinders the overall nar- rative coherence. The results show that our method outperforms existing methods (including the ones with the same video model) in all aspects, emphasizing the effectiveness of integrating cine- matic principles into the automatic film generation system.
F.2 QUANTITATIVE RESULTS ON HUMAN EVALUATION
We show the detailed results of user study in Table 8. Our method shows superior performances compared with other existing film generation systems in human evaluation across 12 dimensions, observations similar Section F.1.
F.3 QUANTITATIVE RESULTS ON VBENCH
We follow (Wu et al., 2025) to report the results of VBench (Huang et al., 2023b) in Table 9, (1) The results demonstrate that our method achieves the highest average score, primarily driven by its exceptional performance in generating dynamic visuals. Our method significantly outperforms all baselines on the "Dynamic Degree" metric, achieving a score of 0.9412. This result quantitatively validates the effectiveness of our Multi-shot Synergized Camera Language Design module in gen- (2) Regarding other metrics such as "Aesthetic
erating diverse and expressive camera movements. Quality" and "Motion Smoothness". our method performs competitively with other systems that use the same video generation model (Kling 1.6). This is expected, as these low-level visual attributes are predominantly determined by the generation model, rather than the high-level planning modules that represent our primary contribution (camera language and cinematic rhythm). (3) is crucial to note, however, that VBench is primarily designed to evaluate low-level video generation quality. currently lacks the capability to assess high-level cinematic aspects such as the narrative coher- ence of camera language or the effectiveness of cinematic rhythm and editing. Therefore, they do
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Table 8: Quantitative comparison of different methods across 12 evaluation criteria. The "Avg" column shows the average score each method. denotes commercial product. denotes the same video model applied. Values in bold indicate the best performance in that column.
NS 1
AT 1
AE 1
RF
EE T
OE
Method
Avg t
SF
NC
VQ
CC
PLC
VIAQ
CT
AVR
NP
VAC
CD
OQ
Anim- Director
2.08
1.80
2.52
2.24
2.32
1.56
2.20
1.68
2.64
1.60
2.12
2.36
2.09
Anim-Director'
2.04
1.84
2.60
2.56
2.48
1.76
1.68
1.20
2.16
1.72
1.84
2.20
2.01
MovicAgent
1.73
1.40
1.67
1.73
1.73
1.40
1.93
1.47
2.00
1.40
2.20
2.27
1.74
MovicAgent'
1.48
1.16
2.76
2.64
2.36
1.76
1.76
1.60
2.04
2.00
1.96
1.92
1.95
"tudio"
2.44
2.12
3.00
2.84
3.16
2.92
2.60
3.20
3.16
Ours
3.73
3.67
3.93
3.53
3.87
3.73
3.87
3.93
3.53
3.93
3.87
3.79
Table Quantitative comparison of different methods VBench. The "Avg" column shows the average score for each method terms aesthetic quality, background consistency, dynamic degree, motion smoothness, subject consistency. denotes commercial product. denotes the same video model applied.
Method
Aesthetic Q.
BG Consist. f Dynamic Deg.
Motion Smooth.
Subject Consist. ?
Avg. 7
0.6980
0,8410
0.1500
0.9638
0.7239
0.6753
Anim- Miin-Director'
0.6808
0.9611
0.0866
0.9943
0.9515
0.7349
AgvieAgent
0.5800
0.9390
0,3500
0,9882
0.7053
0.7125
MovicAgent*
0.6118
0,9503
0.2000
0,9942
0.7041
0.6921
LTX-Studio"
0.6303
0,9464
0.4591
0,9919
0.6471
0.7350
Ours
0.6352
0.9375
0.9412
0.9848
0.8939
0.8785
not fully capture the core strengths of our system in automated filmmaking. The advantages of our method are demonstrated in our qualitative comparisons, the proposed quantitative comparisons and user studies.
F.4 HUMAN CORRELATION COMPARISON
To validate our proposed automatic evaluation metric, assess its correlation with human judg- ments. Following established practices (Park et al., 2021; Huang et al., 2023a), we compute three correlation coefficients across twelve criteria in Table 10: Pearson's Kendall's т, and Spearman's The results show that the correlation of the proposed automatic evaluation achieves an average of 0.5837, demonstrating high alignment with human judgments. We compare our automatic eval- uation against several baselines, as shown in Table 1.. Our proposed evaluation metrics, Gemini for 12 dimensions (Gemini- 12dim), consistently achieve the highest correlation across three co- efficients: Pearson's (0.6185), Spearman's p (0.6013), and Kendall's (0.5312). It significantly outperforms both existing domain-specific metrics like VBench and other MLLMs like GPT-4o, which show much weaker correlation. Furthermore, of the number of evaluation dimensions: as the number of dimensions increases from 1, 6, to our proposed 12, the correlation with human perception consistently improves. This indicates that the comprehensive, multi-faceted nature of our 12-dimension metric is crucial for capturing the nuances of human judgment, which simpler metrics overlook.
provide ablation study on the impact
F.5 ABLATION STUDY
To assess the contribution of each component of our framework, conducted user study with 15 participants. For each experiment, we generated videos using our full model and several ablated versions. Participants were asked to select up to two videos that they felt best adhered to a specific criterion (either "ccamera language" "cinematic rhythm").. We report the preference rate (i.c., the percentage total votes received) for each model variation in Table 12.
Effectiveness of Shot Re-planning, RAG and Scene-Level. We evaluate the core components of our Generation Stage. Participants were asked to select videos with the most professional and effective camera language, given the narrative objective designed by scene. As shown in Table 12, the version without shot re- re-planning saw its preference rate drop by 47.75% compared to the full model. The impact of the RAG module even pronounced, with its removal causing
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Table 10: Quantitative results of correlation coefficients across 12 evaluation criteria in FilmEval and their
average in terms of Pearson Correlation Spearman's 's and Kendall's Coefficient (p-value < 0.01). Green represents the average of each correlation coefficient across 12 evaluation criteria.
Correlation
N8
AT
AE
RF
EF
OF
NC
VQ
PLC
V/AQ
CT
NP
0Q
Avg
Pearson's s (?)
0.6119
0.6576
0.4678
0,3891
0.5317
0.7333
0.6884
0.7470
0.6308
0.6918
0.6718
0.6007
0.6185
Spearman's 0.5589
0.6073
0.4602
0,3886
0.5328
0.7386
0.7203
0.7149
0.5867
0.6791
0.6469
0.5808
0.6013
Kendall's (f)
0,4870
0.5164
0.4260
0.3444
0.4855
0.6584
0.6415
0,6384
0.5047
0.5946
0.5715
0.5067
0.5312
Table 11: Comparison of correlation cocfficients between model predictions and human judgments. Higher values indicate stronger correlation and better performance. Values in bold indicate the best performance for each metric.
Metric
GPT-4o
VBench (avg)
Gemini-ldin
Gemini-6din Gemini-12din (Ours)
Pearson's is (1)
0.2194
0.5452
0.4393
0.4724
0.6185
Spearman's p (t)
0.1385
0.4536
0,5485
0.4911
0.6013
Kendall's x (t)
0.1116
0.3574
0,3822
0.4067
0,5312
83.43% decrease in preference. This shows the effectiveness of referencing proper cinematography from large corpus of film data. Shot re-planning the shot-level decreases by 73.97% compared to scene-level, which shows the effectiveness for synergized shots within at the scene-level. These results underscore the critical role of RAG-based scene-level shot re-planning in generating high- quality camera work. Figure 8 (row 1-4) shows the qualitative comparison for the scene with the narrative objective, which requires emphasizing the insignificance of the Little Prince and the White Fox against the universe. W/o shot re-planning and w/o RAG utilize a more static camera language, w/ RAG the shot-level produces more disjoint and incoherent shots ("orbit" then "pan across", lack the coherence between shots visually), while ours (w RAG at the scene-level) utilizes a richer and more coherent camera language to represent the scene: (I) "a wide sweeping shot of vastness of space". (2) "slowly zooming in on a small rocky asteroid where the Little Prince stands", (3) "camera shifts to steady close-up of the White Fox", and (4) "camera pulls back showing the duo's smallness against the universe".
Impact of Audience-Aware Components. We analyze the effectiveness of our Coordination Stage designed to tailor the output for specific audience. We use ablated versions by removing the audience profile, the video editing pipeline (structural reorganization and duration adjustment), and the sound design. When participants evaluated the videos for cinematic rhythm, our full method was consistently preferred. It outperformed the various ablated versions by a margin ranging from 34.21% % 83.42%, demonstrating that our Audience-Aware Cinematic Rhythm Control is essential for creating a compelling final product.
Influence of Dataset Size. We investigate the relationship between data scale in Generation Stage and perceived output quality. The model using our full dataset was strongly preferred over those using smaller subsets. It received 94.35% more preference votes than a model using only 0.1k sam- ples and an average of 44.56% more votes than models using 10k samples. This highlights the significant benefits of large-scale, high-quality dataset. Figure 8 (row 1, 4-6) presents qualitative comparison for a scene where the narrative objective is to convey the insignificance of the charac- ters. With only 0.1k reference samples, the method fails to maintain scene coherence, incorrectly transitioning to different scene. Increasing the dataset to 1 and 10k samples improves coher- ence, but the resulting camera language is suboptimal and fails to effectively portray the characters' insignificance.
Robustness of RAG Method on Different LLMss To validate that the effectiveness of our RAG method is not dependent on single LLM, we test it with two different LLM backbones: DeepSeek- V3 (Liu et al., 2024) and LLaMA-3 70B (Touvron et al., 2023). Across these differentt LLMs, our RAG-enhanced method maintains strong performance, achieving an average preference improve- ment of 55% over a RAG baseline. This demonstrates the generalizability and robustness of our proposed method.
To validate our method's generalization to open-source models, we conduct two experiments: 1) replace Kling 1.6 video generation model with Wan 2. 1-VACC (Jiang
Using Open-Source Models.
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Table 12: Ablation studies and component analysis. We evaluate the impact of key components by removing them one at time ("wfo"). The best-performing setting each group highlighted bold.
Ablation
Score
Ablation on Shot Re-plan, RAG, and Scene-Level
w/o Re-plan
0.267
w/o RAG
0.100
w/ RAG, Shot-Level
0.133
w/ RAG, Scene-Level (Ours)
0.511
Ablation on Audience Dimensions
w/o Profile
0.250
w/o Structure
0.063
w/o Timing
0.210
w/o Audio
0.100
Ours
0.380
Effect of Dataset Size
0.1k Samples
0.026
1K Samples
0.300
10k Samples
0.210
440k Samples (Ours)
0.460
Effect of Different LLMS for RAG
Baseline
0.180
DeepSeek-V V3
0.360
LLaMA-3 70B
0,450
Table 13: Ablation study on other video generation model and MLLM model. Our method can generalize to other backhones. denotes commercial product. Blue represents the derived metric for CL., and green stands
for CRh.
NS 1
AT
AF. t
RF1
EE
OF
Derived f
Method
Avg
CC
V/AQ
NP
VAC
0Q
CL
MovieAgent
1.60
2.20
4,20
1.45
3.55
1.00
3.05
2.50
2.10
1.00
2.45
2,30
2.96
1.94
2.45
Anim- Director
3.40
2.24
LTX-Studio
2.50
3.00
4.95
4.10
3.90
3.10
4.10
3.85
3.15
4.10
3.65
3.75
3.74
3.62
3.68
Ours (Wan2.1- VACCE)
4.00
4.33
4.33
4.67
4.00
4.33
3.33
3,33
4.00
4.33
3.67
4.00
4.11
3.94
4.03
Ours (Qwen-Vi VL)
4.00
4.50
5,00
5.00
4.00
4.00
3.50
4.50
4.00
5.00
4.50
4.50
4.42
4.33
4.38
ct al., 2025) model. 2) replace Gemini (MLLM) with Qwen (Bai al., 2025) model. We show the automatic evaluation FilmEval in Table 13.
F.6 QUALITATIVE RESULTS
Synergized Camera Language Design. We show qualitative results of scene-level synergized shots for certain narrative objective, detailed with the re-planned shots in Fig- ure 10 and Figure 11. In Figure 10, the scene is composed of four shots (from Script Elaboration) with the narrative objective of "introducing Godzilla's presence and power". The shots are re- planned synergistically with camera language: (1) wide-angle shot to show the environment, the camera panning upward to emphasize Godzilla's towering height; (2) a close-up shot of its razor- sharp eyes, the camera zooming in to accentuate the depth and focus of its gaze; (3) the camera pulling back to capture the powerful echoing: (4) a tracking shot to mirror its pace, the handheld camera to show the raw power and weight of its stride. In Figure 11, the scene, composed of two shots, aims to show the narrative objective of "showcasing the Cybertruck's 's maneuvering to seek safety". The shots re- planned with camera language by: (1) lower angle to capture the struggle and resilience against sands; (2) the camera shifting to a bird's eye view to show its maneuvering into the narrow gap.
Results of Multi-shot
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Objective: Emphasize the insignificance of the Little Prince and the White Fox against the backdrop of the universe. Prompt Summary: Morning, asteroid, Prince White standing quietly with wide eyes, stars twinkling.
W/ BAG, Scent-Levell 44k Samples (Ours)
W/o plan
Shot
Samples
10k Semples
Figure 8: The qualitative results of the ablation study (effectiveness of shot re planning and RAG, and influence dataset size.) Ours (w RAG and 44k samples) shows the alignment with the narrative objective of the scene, which emphasizes the insignificance of the Little Prince and the White Fox against the backdrop of the universe. For the effectiveness of shot re-planning and RAG (row 1 1-4), ours utilizes the richer and coherent camera language to represent the narrative objective: (1) "begins with wide, sweeping shot of the vastness of space", (2) "slowly zooming steady close-up of the White Fox sitting next to him", and (4) "camera then pulls back, showcasing duo's 'ss smallness against the infinite universe". For the influence of dataset size (row 1, 4-6), ours conveys an optimal camera language while maintaining scene consistency: with only 0.1k reference samples, the method fails maintain scene coherence, incorrectly transitioning a different scene. Increasing the dataset Ik and 10k samples improves coherence, but the resulting camera language is suboptimal and fails effectively portray the characters' insignificance.
small, rocky asteroid where the Little Prince stands", (3) "camera shifts
Frame
Frame 66
Frame
Frame 100
Wo Lip-sync
W Lip-sync
Look, it's starting to shimmer, like us.
Figure 9: Qualitative results of the lip synchronization. Our method can casily extended incorporate an off-the-shelf synchronization module after the sound design to achieve lip synchronization.
Comparison with Baselines. We present a qualitative comparison with baseline methods in Fig- ure 12. Compared to our method, the baselines tend to produce static camera language. To ensure fair comparison, equip the baselines with the same video generation model ours (Kling 1.6). The results show that this primarily improves motion quality and ine-grained details (e.g., hand generation), rather than introducing more dynamic cinematic techniques. This demonstrates that the cinematic techniques are improved by our proposed Multi-shot Synergized Camera Language Design, not just the underlying video model.
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Objective: Introduce Godzilla's presence and
Prompt Summary: Day, desert, Godzilla looming and roaring, grounds shakes.
Shot Begin with wide-angle
desert's edge, capturing the shimmering rising off the sand. Gradually, Godzilla's imposing silhouette appears on the horizon, with the camera panning upward to emphasize its towering height. Its rough, aged scales catch the sunlight, glinting like ancient armor and casting intimidating shadows across the landscape.
Shot 2: Transition an intense close-up shot Godzilla's eyes, razor-sharp and of Intent. The camera zooms dramatically, accentuating depth and focus of gaze as it scans the barren landscape with purpose that suggests deliberate evaluation.
As Godzilla releases thunderous roar, the camera pulls back capture powerful reverberation echoing through the arid environment. The roar's impact heightened with subtle slow motion effect, and the ground visibly trembles, sending ripples through the sand as distant dunes shake.
Shot 4: Follow Godzilla's 's steps with tracking shot that mirrors deliberate pace. The ground vibrates with each massive footfall, and the handheld camera movement communicates the power and weight of its stride. Dust swirls up around its feet, enhancing the tense atmosphere that accompanies Godzilla's advancing presence.
Figure 10: Qualitative results of Multi-shot Synergized Camera Language Design. The scene is composed of four shots (from Script Elaboration) with the narrative objective of "introducing Godzilla's presence and power". The shots are re-planned in synergized manner with camera language.
Results of the Final Generated Films. We provide more generated films in Figure 13, Figure 14, Figure 15, and Figure 16. The qualitative results show that our proposed FilMaster can provide end-to-end film generation process, accomplishing a coherent film results based on the input of text, reference images of characters, and locations. Our proposed method can also handle different length of input texts and different number of reference images.
Results L snpSSynnhronizationn Our method can easily extended to incorporate an off-the-shelf synchronization module after the sound design to achieve lip synchronization. Here we use Kling model (Kling, 2025) to synchronize the lip the Little Prince, with the VO "Look, it's starting to shimmer, like us" as shown in Figure 9.
G FILM DATASET
To ground our system's cinematographic knowledge in real-world examples, a large-scale film dataset was constructed for internal research purposes. The source films were segmented into short clips of 5 and 10 seconds in duration. Then an MLLM (LLaVA (Xu et al., 2024)) was employed to generate textual descriptions for each clip. These descriptions capture key cinematic elements, including camera language, character actions, and scene setting, forming the basis of our retrieval database. Our final dataset consists of approximately 310,000 5-second clips and 136,000 10-second clips. The generated textual descriptions have average length of 231 words per clip.
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Objective:
Showcase the Cybertruck's maneuvering seek safety,
Prompt Summary: Day, desert, Cybertruck evading, finds rock shelter, low visibility.
Shot Cybertruck navigates through fierce sandstorm, camera follows closely lower angle, capturing the struggle and resilience of the vehicle against the swirling sands. The atmosphere around is tense and chaotic, with the sound of the wind howling and sand grains pelting against the truck's body, creating sense of urgency and endurance. The visibility is low, with grains sand creating hazy curtain, but the Cybertruck's silhouette is clearly highlighted against the dusty, stormy backdrop.
Shot 2: The camera shifts to a bird's eye view as the Cybertruck discovers narrow gap between towering rock formations and carefully maneuvers into this protective enclave. The atmosphere shifts from chaotic to slightly calmer, with the howling wind subdued within the rock barriers, giving sense of relief and safety. Dust particles still dance in the filtered light, creating serene yet dramatic effect, as the Cybertruck comes to rest in a safe position.
Figure 11: Qualitative results of Multi-shot Synergized Camera Language Design. The scene, composed of two shots, aims show the narrative objective of "showcasing the Cybertruck's maneuvering seek safety". The are re-planned with camera language synergistically.
Deector
Movistgent
Movistgent
Stuilo
Oust
Figure 12: Qualitative comparison of film generation in the case of "litde prince".. Ours show more expressive camera language, compared to Anim-Director, MovieAgent, and LTX-Studio. denotes the same video model (Kling 1.6) applied.
H (M)LLM INSTRUCTIONS
We provide the prompts used in (M)LLMs for illustration. For script elaboration, we provide the LLM with the examples in Figure 17 and Figure 18. For Multi-shot Synergized Camera Lan- guage Design, show in Figure 19. For Awwee Cinematic Rhythm Control, we provide audience-awan review in Figure 20, video editing in Figure 21- Figure 23, sound design in Figure 24 and Figure 25.
DISCUSSION ON CINEMATIC STYLES
Varied Cinematic Styles. Our method can generate varied cinematic styles. Retrieval used to en- hance cinematic coherence, such maintaining proper shot grammar, but does not override the style
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Input text:
space journey the blond Prince met rose, Prince gave rour shaking its head laugh. Input images:
Asteroid
Spacecraft
Figure 13: Qualitative results of the final generated film for the input text of "Little Prince",
Input Gedvilla Cybectruck engaged desperately escape sandstorm.
footsteps shaking ground,
Input images:
Desert
Godallla
Cybertruck
Figure 14: Qualitative results of the final generated film for the input text of "Cybertruck hattle with Godzilla".
instructions given to the model. The generator balance retrieved cues with style-conditioning inputs, ensuring creative freedom. Increasing retrieval diversity (e.g., larger K) further reduces po- tential bias. We demonstrate this flexibility through two examples in Figure 26. First, show
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Input text:
filey enters teenage years, mine's Headquarters undergoes sudden transformation, welcoming new Fmotions, Anciety takes charge, belieuing constant wony will help filly navigate ado escence, while Eriyy fuels seli-duubt, Joy, Sadness, Anger, Fear, and Disguet struggle to adapt as Arwety's influence grows, sidelning their roles. As Kiley faces mounting pressure, the imbalance leads to emotional turmoll. Realizing that Ansicty alone cannot define fley's experience, the onggral Emotions work to restore balance, the end, kiley embroces complexties of growing up, with both old and new Emotions leamnng to coesst..
Input images:
Bedroom
Dining
Study
Figure 15: Qualitative results the final generated film for the long input text of "Rilcy".
stylistic adaptation by transforming reference images into a "Ghibli style" using an off-the-shelf model, effectively shifting the outputs from generic animation specific Ghibli look. Second, we demonstrate that increasing the number of retrieved examples to K=10 allows for more relevant video clips, thereby diversifying the camera design. For instance, given the narrative objective "em- phasize the insignificance of the Little Prince and the White Fox," the model yields "close-up then pull back" with K=3, whereas produces "pan around with wide camera angle" with K=10.
Generalization to Unseen Domains. Our method demonstrates generalization capabilities in un- seen domains, such as the documentary genre. Quantitative and qualitative results are presented in Table 14 and Figure 27, respectively. Given the input text "A documentary look at life in the desert and how living things survive the extreme environment," our method first elaborates script featuring relevant characters serving as observers, such as a local biologist, wildlife photographer, a storytelling Bedouin elder, and a passionate botanist. Subsequently, it generates videos incorpo- rating appropriate camera language and cinematic rhythm.
Table 14: Quantitative comparisons on documentary v.s. non-documentary genres.
Method
CL (f)
CRh (1)
Avg (1)
Ours (non-documentary)
4.50
4.32
4.41
Ours (documentary)
4.50
4.17
4.33
J FAILURE CASES
Consistency with occluded or unseen parts. Consistency across shots can still be challenging, especially when reference images contain occluded unseen parts Figure 28-(a).
Inter-scene camera work transition. While our method models multi-shot camera language within scene, inter-scene camera transitions remain unexplored Figure 28-(b). Achieving smooth transi-
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Input text:
quietly heard footsteps outside
Input images:
Surroundings
Home
John man)
Figure 16: Qualitative results of the final generated film for the short input text of "He heard footsteps".
tions requires the MLLM to plan shared visual elements across scenes, which we leave for future work.
Large-motion distortion from the base video model. For scenes involving fast or complex motion, the underlying video generator may produce distortions. These artifacts are inherited from the base model and remain an open challenge.
K LIMITATIONS
While FilMaster represents a significant step towards automated professional film generation, cur- rently has certain limitations. 1) It inherits the limitations of current (M)LLMs. For example, current MLLMs operate at a relatively coarse temporal granularity (at the level of "second"), while more precise sub-second alignment required for foley and sound effects design. This may cause audio- video mismatched issues. 2) Dialogue-heavy scenes are challenging, and our current system does not perform well here. There are two main reasons: (a) Shot/reverse-shot structure: Dialogue scenes require stable spatial layout (e.g., speaker A always on the left, speaker B on the right across cuts). This requires strong character-position tracking, which our system does not yet model. (b) Lip syn- chronization: Accurate lip-sync is essential in dialogue-heavy content but is not the primary focus of our method. 3) Advanced post-production techniques such as color grading and a diverse range of cinematic transitions are not yet incorporated into system. These aspects, crucial for achiev- ing fully polished cinematic look and feel, were beyond the primary scope of this work, which focused on foundational camera language design and cinematic rhythm control. We acknowledge their importance and plan to address their integration in future research.
L USE OF LLMS
We used LLMs to improve the grammar, spelling, and clarity of our writing. The authors reviewed and edited all text and responsible for the final content.
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LLM Prompt for Script Elaboration
Overall System Prompt: You are a professional director. You are going write a script from 4 steps (I. Synopsis, 2. Simplified Storyboard, 3. Detailed Storyboard, 4. Scene) hierarchically, Never use Markdown.
Step 1: Synopsis
You are in the first step (1. Synopsis). Design synopsis based on the user input. The synopsis must contain:
Theme: The central idea of the story.
Characters: 2-5 characters, each with name and a concrete visual description.
Plot: A summary of the story's s main events.
Example for Step
Input:
He waits anxiously.
LLM Output:
a. Theme:
Depicting a man's anxious and complex state of mind.
b. Characters: Man, Woman (Wife)
C. Plot: A man anxiously waits at the hospital, constantly worrying about his wife's s health. Every time a doctor appears, hopes for answers...
Step 2: Simplified Storyboard
You are the second step (2. Simplified Storyboard. Design a simplified storyboard based the synopsis, structured into four parts: Background, Cause, Development, and Outcome.
Example for Step
Input (Output from Step 1):
a.
Theme: Depicting a man's s anxious and complex state of mind...
LLM Output:
a.
Background: He is a progranmer. Due to long hours of overtime...
b. Cause: He nervously sits in the hospital waiting area, gripping...
C. Development: He stands at the door of his home, tightly gripping...
d. Outcome: The next morning, the man received call from the doctor...
Figure 17: The illustration of prompts used for script elaboration. The process starts with high-level synopsis and progressively refines through four major steps into detailed, structured storyboard, Here the first 2 steps are shown.
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LLM Prompt for Script Elaboration
Step 3: Detailed Storyboard
You are in the third step (3. Detailed Storyboard). Extend the simplified storyboard with detailed visual descriptions, ensuring each sentence describes simple, concrete 5-second shot. The visual descriptive writing can include both real and imaginary scenes. Real scenes consist of primary shots (specific descriptions) and reverse shots (descriptions of the sur- rounding environment), while imaginary scenes include unrelated shots (metaphors).
Example for Step 3
Input (Output from Step 2):
a. Background: He is a programmer...
LLM Output:
a:
i. In office, he is programming, his eyes fixed the computer...
ii. His stomach growls loudly, and he winces in pain, his hand...
1. In a corner of the hospital, he curls up on a faded b:
blue bench...
Step 4: Scene
This final step involves segmenting the detailed storyboard into distinct scenes with the same spatio-temporal contexts: Group prompts chronologically and by location, extracting metadata (Time, Location, Characters, Visual Elements, Objective), adding detailed audio descriptions (voice-over, background ambiance, musical scoring) to each scene as place- holder.
Example for Step
Input (Output from Step 3).
LLM Output:
a. Time 1: Space 1:
Summary: Evening, office, Man (focused, working, programming on a computer).
Prompt:
(1) A man sits in a dimly lit office. His face is illudinated by the blue glew of computer screen. His fingers type rhythmically on the keyboard. (2) The man suddenly winces, clutching his stomach He leans back, rubbing his temples as the room seems spin.
Time: Evening; Location: Office; Character: Man;
Visual Elements: None;
Objective: Emphasize the man's daily routine of overworking.
Audio:
(1) voice-over: (None)
(2) background ambiance: Quiet hum of comp- uter fan, distant city traffic. (3) music: Low, monotonous synth track sugg- esting routine.
Figure 18: The illustration of prompts used for script elaboration. The process shows the detailed storyboard and the final output, which is organized into scenes with spatio-temporal contexts.
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LLM Prompt in Multi-shot Synergized Camera Language Design
The following instructions are provided to the LLM to enrich given shot description with einematic details based on retrieved examples.
Your role is to add the camera movements and the atmosphere for each prompt.
Leave the "Time, Location, Character, Visual Elements, Object ive" intact. You only need to revise the "Prompt" part.
You can retrieve the similar prompts in the dataset, and design suitable camera movements and atmosphere that correspond the scene.
Use similar templates as in the retrieved prompts to rewrite the prompts.
Think step by step, and check your answers.
Never use Markdown.
Here is the user prompt: (user_prompl)
Note: The placeholder (user.promp) is dynamically replaced with the textual descrip- tion of the scene.
The illustration of prompts used for retrieval and shot re-planning after spatio-temporal-awee in- dexing our Multi-shot Synergized Camera Language Design module. The LLM instructed to act as cinematographer, using retrieved examples from film dataset as reference to rewrite shot prompts within the scene with professional camera language and atmospheric details.
Figure 19:
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MLLM Prompt for Audience-Aware Review
The MLLM is instructed to act as simulated audience with specific persona. This prompt enables generating the critical feedback that drives the refinement from Rough Cut Fine Cut.
You are movie viewer, with these demographic profile: httpograapph_ppofieeeee
You need to evaluate the biggest problem of the video in the aspect of video structure, rhythm, scene transitions, and audio.
Here is the script: (script)
Here is the audio: (audio)
Pay attention that the main idea and plots in script should not be changed, only the representation in structure, rhythm, scene transitions, and audio be changed.
Placeholders:
{demographic profile]: A description of the target audience's characteristics and viewing preferences, which generated by an LLM leveraging search tools. Here we use "short-drama audience".
[script]: ): The input text.
(audio): The initial placeholder audio descriptions (VO) that accompany the video clips.
illustration prompts used to generate audience-aware feedback. The MLLM embodies specific audience persona to critique a rough cut, focusing on structural and rhythmic elements rather than the core plot. This feedback is the primary input for the subsequent video editing and sound design.
Figure 20:
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Prompting Pipeline for the Vidco Editing
The video editing process guided by a sequence prompts. First, an MLLM generates a detailed, time-coded description of the video. This description then serves key input for Iwo parallel LLM tasks that plan structural and durational adjustments.
Step A: MLLM-based Video Description Generation This prompt instructs MILM to create detailed, time-coded caption for each 5-second shot, providing structured representation of the video content.
Describe the video based on the script, audio, and the video.
Here is the script of the video: (script)
Here is the audio of the video:
(audio)
For each scene (5s) in this video, generate detailed cap- tions that describe the scene along with any spoken text placed in quotation marks. Place each caption into an object with the timecode the caption.
Output example:
"timecode": "00:00-00:05",
"caption"; "A dirt path winds through sunlit forest..." th
Figure prompting pipeline the Video Editing. (A) An MLLM first annotates video with detailed, time-coded descriptions. (B) This structured description, along with the audience analysis, is then fed into two parallel LLM prompts that generate plans for Structure Adjustment and Duration Adjustment, forming the complete editing plan for the Fine Cut.
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Prompting Pipeline for the Video Editing
Step B: LLM-based Editing Plan Generation
Using the MLLM's output ((video descript: on)) and the audience analysis (fanalysis)), two parallel prompts generate the final editing plan.
Editing Task 1: Structure Adjustment
are good strueture adjustment for the video. The main idea provided by the audio should be kept. For each shot (5s) in this video, help me to reorder the shots ba- sed on the video and the analysis.
Here is the description of the video: sridee_descripptonn
Here is the audio for the video (each paragraph is a shot) (audio)
Here is the analysis: (analysis)
Requirements:
(1) Split the shots into scenes.
(2) Construct the structure of the video in the format of timeline.
(3) Reorder the structure based on the analysis.
Figure 22: The prompting pipeline for the Video Editing. (A) An MLLM first annotates the video with detailed, time-coded descriptions. (B) This structured description, along with the audience analysis, is then fed into two parallel LLM prompts that generate plans for Structure Adjustment and Duration Adjustment, forming the complete editing plan for the Fine Cut.
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Prompting Pipeline for the Video Editing
Editing Task 2: Duration Adjustment
You are good at time duration adjustment for the video, For each shot (5s) in this video, numerate the shot, and help me to suggest
the time duration (<=5s) for each shot based the video and the analysis, which enables the overall pacing.
Here is the description of the video: saideo_descriptine
Here is the analysis: (analysis)
You need to determine the duration of each shot (5s), whe- ther the video should be cropped directly, accelerated to retain the original dura-
the suggested time duration, tion (5s) to maintain overall pacing. If the suggested duration is 5s, the mode is "retain".
If less than 5s, choose "crop" or "accelerate":
-"Crop" is used for cutting down tedious information. -"Accelerate" is used for speeding up the video for pacing.
Check your answer and give the reasons.
Output example:
{(
"shot 1": "00:00-00:05",
"duration"; "suggested time duration in second", "mode": "crop, accelerate, or retain", "reason": "xxx"
}
Figure 23: The prompting pipeline for the Video Editing. (A) An MLLM first annotates the video with detailed, time-coded descriptions. (B) This structured description, along with the audience analysis, is then fed into two parallel LLM prompts that generate plans for Structure Adjustment and Duration Adjustment, forming the complete editing plan for the Fine Cut.
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Prompting Pipeline for Multi-Scale Sound Design
The sound design process employs a multi-scale strategy, using distinct prompts to generate different audio layers at their appropriate temporal granularity.
This prompt instructs LLM to generate the vocal track for the film, operating at the shot level (e.g., 5-second segments).
Task 1: Shot-Level Sound Design
You are good at adding audio (narration and dialogue) for the video. For this video, help me to add narration or dialogue based the video and the analysis. You can group shots into scenes if they describe the same thing.
Text Requirements:
Narration and dialogue should not appear simultaneously.
The total word number should be controlled to 1 word per second.
Check the word number: Ensure the total number of words appropriate for the time frame (e.g., words for 10s scene).
Here is the script of the video: (script_simple)
Here is the description of the video:
Here is the analysis: (analysis)
Output example: {(
"time": "00:00-00:05", "narration": "xxx", "dialogue": "character: xxx", "word number": 1b
Figure 24: The prompting pipeline for multi-scale Sound Design module. The process is split into two parallel tasks operating different temporal scales. (Task 1) An L.M generates shot-level narration and dialogue, adhering to strict length constraints. (Task 2) An MLLM generates intra-shot, fine-grained foley and sound effects synchronized with specific visual actions. Together, these prompts create a rich, multi-layered soundscape.
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Prompting Pipeline for Multi-Scale Sound Design
This prompt instructs an MLLM to generate fine-grained folcy and sound effects by analyz- ing the video content on a second-by seonddd----oondd basis.
Task 2: Intra-Shot Sound Design
Here is the caption of the video: Please describe the actions in this video frame by frame with the timecode range. Give the suitable foley and sou- ndeffect to the frames described.
Output example:
[
{(
"00:00-00:01" "The camera follows the character from be- hind as they begin to walk along path into the forest. "Foley": "Footsteps on a soft forest path with occasional crunching sounds. Gentle rustling of leaves." "
"Soundeffect": "Light magical twinkle or chime to empha- size the forest ambiance" 1b, {(
"00:01-00:02": xxx 1),
Answer in json directly.
The prompting pipeline our multi-scale Sound Design module. The process split into two parallel tasks operating at different temporal scales. (Task 1) An LLM generates shot-level narration and dialogue, adhering to strict length constraints. (Task 2) An MLLM generates intra-shot, fine-grained folcy and sound effeets synchronized with specific visual actions. Together, these prompts create rich, multi-laycred soundscape.
Figure 25:
Objective: Emphasize the insignificance of the Little Prince and the White Fax against the backdrop of the universe.
Prompt Summary: Morning, asteroid, Prince and White standing quietly with wide eyes, stars twinkling.
Original K+3
Original
Ghiddi-Style
The camera shifts to steady close-up of the White Fox sitting next to him, its fur rippling the light breeze, illuminated starlight. The camera then pulls back, showcasing the duo's smallness against infinite universe
shibl-sttyee 6-3
The camera then gently pans around, focusing the Prince. The fur of the Fax moves slightly in the tranquil cosmic breeze. The wide camera angle further underscores their smallness against the sprawling universe.
Original K-10
White sitting quietly
Ghilli-Style k-10
Figure 26: Varied cinematic style examples. We show our method can generate varied cinematic style videos by (1) changing the styles of reference images; (2) increase the retrieved number K.
43

---

Published as a conference paper at ICLR 2026
Input
lving things
Input
Desert
local blorogit
Awiddife photographer A Bedouin elder with lietime of stories
passionate botanist
Figure 27: Our method can generalize to unseen domains, such as documentary genre. Given the input text "A documentary look at life in the desert and how living things survive the extreme environment," our method first elaborates script featuring relevant characters serving observers, such local biologist. wildlife photographer, a storytelling Bedouin elder, and passionate botanist. Subsequently, it generates videos with camera language and cinematic rhythm.
(3) Inconsistency caused by occluded unssenn parts
(b) Canatural inter-scene camera work transition (frame 145 and 146)
Figure 28: Failure cases of FilMaster. (a) Inconsistency caused by occluded unseen parts in reference images. Here, the color of the Little Prince's shoes changes from white brown. (b) Unnatural inter-scene camera work transition. The visual focus shifts abruptly from the center frame 145 the left (White Fox) frame 146, resulting a jarring discontinuity.
44


Reviews:
[{"review_id": "yqYiPIJ842", "reviewer": "Reviewer_Pb14", "summary": "The paper aims at automated film video generation, i.e., automatically generating film videos given a text and sets of reference images for characters and locations as input. The proposed framework, named \"FilMaster\", attempts to surpass existing methods in terms of camera language and cinematic rhythm. The core contributions lie in the introduced Multi-shot Synergized Camera Language Design and Audience-aware Cinematic Rhythm Control, where the former is designed for camera language by introducing a shot-level RAG, and the latter module serves as a post-production process to refine the \"Rough Cut\" to \"Fine Cut\" for cinematic rhythm. FilMaster is evaluated on the proposed FilmEval benchmark and outperforms recent methods (Anim-Director, MovieAgent, and LTX-Studio) in both camera language and cinematic rhythm, which is further evidenced through a user study. The paper also presents ablation studies regarding the two proposed modules.", "questions": "It remains unclear whether the system can produce varied cinematic styles or if retrieval biases constrain creativity?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper tackles an important and underexplored area—bridging cinematic principles and AI-based film generation. The topic is both academically relevant and practically impactful.\n2. The authors explicitly ground their work in film principles (camera language, cinematic rhythm, audience perception, etc.) and emulate professional filmmaking workflows. This fills an existing academic gap between generative modeling and film studies.\n3. The scene-level retrieval and coordinated camera planning improve shot-level incoherence in previous RAG-based methods.", "weaknesses": "1. While the system design is well-engineered, it lacks a deep technical innovation or theoretical insight at the algorithmic level. I would prefer to see more technically substantive modules rather than a purely workflow-oriented system.\n2. The system’s multi-stage pipeline (retrieval -> shot planning -> rough cut -> audience feedback -> fine cut -> sound production) introduces fragility. The paper doesn’t investigate how errors propagate or how robust the system is when upstream stages fail.\n3. Despite providing reference images, generated scenes may still lack consistency across shots, especially for partially occluded or unseen parts of reference subjects/scenes. The paper does not discuss this, though it is crucial for multi-shot storytelling.\n4. Some generated results in the supplementary video exhibit noticeable audio–video misalignment, including unsynchronized sound effects and inaccurate lip movements.\n5. The workflow depends on GPT-4o, Gemini, and Kling video generators. This makes reproducibility extremely difficult and limits transparency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NMePzi4xQT", "reviewer": "Reviewer_uN4o", "summary": "This paper introduces FilMaster, an end-to-end automated film generation system designed to address key limitations in existing AI-based video generation. FilMaster proposes a Multi-shot Synergized Camera Language Design module which enables scene-level RAG that learns cinematography from movie references. To achieve a professional narrative, FilMaster further introduces Audience-Aware Cinematic Rhythm Control module for post-production workflow. The visualization experiments provide qualitative evidence for the effectiveness of the proposed method.", "questions": "Please See Weaknesses", "limitations": "", "rating": 2, "confidence": 5, "soundness": 2, "presentation": 1, "contribution": 1, "strengths": "- The overall method is easy to follow.\n- The visualizations in the supplementary materials offer clear qualitative support for the method's effectiveness.", "weaknesses": "**1. Limited Novelty**\n\nI do not typically raise concerns about novelty lightly, but I must state that the technical contribution of this paper is highly limited. At its core, the proposed method is a relatively straightforward application of RAG. Crucial generative capabilities, such as identity preservation and high-fidelity video synthesis, appear to be inherited from the underlying foundational models used, rather than being novel contributions of the FilMaster framework itself. While the academic community certainly welcomes simple yet effective methods, such contributions usually offer a fundamental insight into the problem being studied. I do not believe this paper achieves that. Instead of solving a core technical challenge, the work primarily focuses on orchestrating existing components. For these reasons, the paper reads more like a well-executed technical project than a piece of novel research.\n\n**2. Subjective Evaluation**\n\nThe primary evaluation is based on a user study, which, while valuable, is inherently subjective. Although the authors supplement this with an automatic evaluation using Gemini, this approach is also a form of subjective assessment. The paper would be much stronger if it included more objective and popular metrics. For example, text-video similarity scores could be used to measure the faithfulness of script elaboration, and a quantitative metric for identity consistency across shots would provide more robust evidence of the system's capabilities.\n\nI understand a potential reason why authors did not discuss these metrics might be that these objective metrics would primarily test the performance of the base video generation model, not the proposed framework. However, this argument circles back to the core issue of novelty. If the main contribution is limited to the design of using cinematic language, and the measurable technical improvements are attributable to the underlying models, then the novelty of the framework itself is insufficient.\n\n\n**Conclusion**\n\nThe paper is methodologically sound, but its contribution feels incremental. It does not present significant shortcomings, but conversely, it lacks any clear and compelling advantages that would distinguish it from prior work. The contribution feels unsubstantial and does not appear to meet the bar for acceptance.\n\nI‘m willing to reconsider my score if the authors can provide a convincing rebuttal that thoroughly addresses my concerns.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wN8ZH3375V", "reviewer": "Reviewer_rwQb", "summary": "This paper presents a fully automated, end-to-end clip-level video generation agent. By providing multiple reference images and an initial prompt as input, the system invokes different models to iteratively modify both the prompt and the video. During the Coordination stage, the agent also retrieves suitable audio from an audio library for adaptation. The construction of cinematic language, agent-level video editing (such as acceleration and scene switching), and audio synchronization are particularly novel aspects of the approach. The experimental results, as well as the videos included in the supplementary materials, are impressive and refreshing.", "questions": "See the Weaknesses.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. An automatic end-to-end clip-level video generation agent with impressive performance.\n2. The Coordination Stage could edit the order and duration of the generated videos, which is reasonable as inter-clip videos do not have strict temporal order constraint. The audio fusion manner is also intuitive and suitable for agent like methods (retrieval and synchronized).\n3. The whole paper is well writen and easy to understand.", "weaknesses": "The performance of this paper is great, my main concern lies on fair comparison with existing methods. This paper utilizes Kling 1.6 as the video generation model, which is much better than existing open-source model that previous method used, such as CogVideoX and LTX-Video. So is the performance improvement simply caused by the basic ability of Kling? It is recommended to have a fair comparison with MovieAgent to see the contribution of this paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AizBd2EMLn", "reviewer": "Reviewer_399a", "summary": "The paper proposes FilMaster, an automated system for film generation that integrates cinematic principles—specifically, camera language design and cinematic rhythm control—into the video generation pipeline.\nIt introduces:\n\n* A Multi-shot Synergized Camera Language Design module using a scene-level Retrieval-Augmented Generation (RAG) from a 440k film dataset to plan expressive, coherent shots.\n\n* An Audience-Aware Cinematic Rhythm Control module that simulates post-production editing with “audience feedback” and multi-track sound design.", "questions": "* Could modules (e.g., video generation, editing) be replaced with other backbones besides Kling? How modular is the system?\n\n* What level of human intervention or curation is required—how many outputs are generated per scene, and how is the best one selected?\n\n* What are the typical failure cases (e.g., incoherent motion, desynced audio, unrealistic rhythm)?\n\n* Have you evaluated the generalization to unseen domains (e.g., documentaries, dialogue-heavy content)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Expressive camera work: Produces more natural and film-like results; shows improvement in “camera language.”\n\n* Comprehensive multi-stage pipeline: Covers both pre- and post-production, integrating video, audio, and editing.\n\n* Quantitative and qualitative validation: Outperforms baselines on FilmEval and user studies.", "weaknesses": "* Pipeline complexity: The system is overly intricate, requiring multiple LLMs and retrieval steps; real-time practicality is unclear.\n\n* Manual intervention unclear: The amount of human selection or post-curation is not specified—e.g., how many outputs are generated and chosen.\n\n* Failure cases not discussed: The paper lacks analysis of when and why the method fails (e.g., scene inconsistency, unnatural transitions).\n\n* Audio-visual mismatch: Lip sync and timing issues occasionally appear, affecting realism.\n\n* Aesthetic awkwardness: Some close-up (“in-your-face”) camera shots feel forced or unnatural compared to simpler long-shots like in LTX-Video.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)