Cinematographic Camera Diffusion Model

Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, a...

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Published in:	Computer graphics forum Vol. 43; no. 2; pp. 1 - n/a
Main Authors:	Jiang, Hongda, Wang, Xi, Christie, Marc, Liu, Libin, Chen, Baoquan
Format:	Journal Article
Language:	English
Published:	Oxford Blackwell Publishing Ltd 01.05.2024 Wiley
Subjects:	Artificial Intelligence Artists Cameras CCS Concepts Cinematography Computational Geometry Computer Science Computing methodologies → Procedural animation Virtual environments Cinematography Animation Generative AI Camera control
ISSN:	0167-7055, 1467-8659
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Abstract	Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control.
AbstractList	Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despitean elaborate film grammar, forged through years of experience, that enables the specification of camera motions throughcinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to placeand move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numeroustechniques have been proposed in the literature (optimization-based solving, encoding of empirical rules, learning from realexamples, etc.), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer-based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high-level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text-to-camera motion approach through qualitative and quantitative experiments and gather feedback from professionalartists. Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control . Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control. In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control. Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar, forged through years of experience, that enables the specification of camera motions through cinematographic properties (framing, shots sizes, angles, motions), there are endless possibilities in deciding how to place and move cameras with characters. Dealing with these possibilities is part of the complexity of the problem. While numerous techniques have been proposed in the literature (optimization‐based solving, encoding of empirical rules, learning from real examples,…), the results either lack variety or ease of control.In this paper, we propose a cinematographic camera diffusion model using a transformer‐based architecture to handle temporality and exploit the stochasticity of diffusion models to generate diverse and qualitative trajectories conditioned by high‐level textual descriptions. We extend the work by integrating keyframing constraints and the ability to blend naturally between motions using latent interpolation, in a way to augment the degree of control of the designers. We demonstrate the strengths of this text‐to‐camera motion approach through qualitative and quantitative experiments and gather feedback from professional artists. The code and data are available at https://github.com/jianghd1996/Camera-control.
Author	Liu, Libin Chen, Baoquan Christie, Marc Jiang, Hongda Wang, Xi
Author_xml	– sequence: 1 givenname: Hongda orcidid: 0000-0002-0296-4431 surname: Jiang fullname: Jiang, Hongda organization: National Key Laboratory of General AI – sequence: 2 givenname: Xi orcidid: 0000-0001-6586-1926 surname: Wang fullname: Wang, Xi organization: Team VISTA, LIX, École Polytechnique – sequence: 3 givenname: Marc surname: Christie fullname: Christie, Marc organization: University Rennes, Inria, CNRS, IRISA – sequence: 4 givenname: Libin orcidid: 0000-0003-2280-6817 surname: Liu fullname: Liu, Libin organization: National Key Laboratory of General AI – sequence: 5 givenname: Baoquan surname: Chen fullname: Chen, Baoquan email: baoquan@pku.edu.cn organization: National Key Laboratory of General AI
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Snippet	Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despite an elaborate film grammar,... Designing effective camera trajectories in virtual 3D environments is a challenging task even for experienced animators. Despitean elaborate film grammar,...
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SubjectTerms	Artificial Intelligence Artists Cameras CCS Concepts Cinematography Computational Geometry Computer Science Computing methodologies → Procedural animation Virtual environments
Title	Cinematographic Camera Diffusion Model
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