TKCAM: Text and Keyframe to Camera Trajectory Generation

arXiv:2610.11105 · cs.CV · Submitted 2026-10-08 · Read on arXiv

cs.CV

Submitted: 2026-10-08

Updated: 2026-10-08

Comments: 20 pages, 7 figures. Paper accepted to NeurIPS2026 (submission number 15461)

Code: https://github.com/linearalgebrayhz/TKCAM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Transformer — 33× in this paper · explained in 2 episodes
• Alignment — 13× in this paper
• Quantization — 11× in this paper · explained in 15 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Embeddings — 6× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Diffusion Model — 5× in this paper · explained in 10 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Residual Vector Quantizer — 5× in this paper
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Wasserstein Distance — 4× in this paper · explained in 5 episodes
• Diffusion Models — 3× in this paper · explained in 9 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Absolute Motion — 2× in this paper · explained in 1 episode
• Active Perception — 2× in this paper · explained in 2 episodes
• Multimodal Fusion — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Semantic Alignment — 2× in this paper · explained in 6 episodes
• Structural Integrity — 2× in this paper · explained in 3 episodes
• Transformer Architecture — 2× in this paper · explained in 3 episodes
• Visual Grounding — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Classifier-Free Guidance — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-Attention Mechanism — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Exposure Bias — 1× in this paper · explained in 2 episodes
• Few-Shot Learning — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Modality Gap — 1× in this paper · explained in 6 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Phase Transitions — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Temporal consistency — 1× in this paper · explained in 3 episodes
• Transfer Learning — 1× in this paper · explained in 15 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• A Spatio-temporal Transformer for 3D Human Motion Prediction
• Flamingo: a Visual Language Model for Few-Shot Learning
• Qwen3-VL Technical Report
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Classifier-Free Diffusion Guidance
• Martian World Model: Controllable Video Synthesis with Physically Accurate 3D Reconstructions
• DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
• Decoupled Weight Decay Regularization
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• Neural Discrete Representation Learning
• Vision in Action: Learning Active Perception from Human Demonstrations
• CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
• Qwen3 Technical Report
• Depth Anything V2
• ShotVerse: Advancing Cinematic Camera Control for Text-Driven Multi-Shot Video Creation
• HERO: Human Reaction Generation from Videos
• ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
• T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
• CamI2V: Camera-Controlled Image-to-Video Diffusion Model
• On the Continuity of Rotation Representations in Neural Networks

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