CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation

• URL: http://arxiv.org/abs/2606.13768v2
• Date: Tue, 16 Jun 2026 17:59:39 GMT
• Title: CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
• Abstract summary: We present CineOrchestra, a unified video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously.<n>Our key insight is that these heterogeneous cinematic elements share a fundamental structure.<n>CineOrchestra outperforms six per-axis specialists on dense caption following and shot-transition timing.
• Score: 57.47993858752813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together, these elements demand a level of fine-grained control beyond current text-to-video models. Existing work addresses each axis in isolation: multi-subject personalization, temporal control, multi-shot synthesis, or camera control; no prior framework jointly integrates all four. We present CineOrchestra, a unified video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously. Our key insight is that these heterogeneous cinematic elements share a fundamental structure: each is an entity acting over a specific temporal interval, which can therefore all be expressed through one shared structure of entity-centric conditioning primitives, augmented with reference images for visual entities. This formulation reduces the architectural challenge to a single positional encoding problem, which we solve with two parameter-free coordinated rotary embeddings: (a) an interval-sampled temporal RoPE that yields consistent attention behavior across events of dramatically varying duration, and (b) a 2D entity-temporal cross-attention RoPE that disambiguates per-entity conditions and routes each to its corresponding spatiotemporal region. On two new benchmarks, CineOrchestra outperforms six per-axis specialists on dense caption following and shot-transition timing, with consistent gains in a pairwise user study and component ablations. Project page: https://snap-research.github.io/CineOrchestra

Related papers

• SparseCtrl-HOI: Sparse Temporal Control for Human-Object Interaction Video Generation [29.683204938136758]
  Human-Object Interaction generation aims to synthesize realistic humans manipulating diverse objects.<n>Existing approaches to this problem rely on dense temporal guidance.<n>We introduce SparseCtrl-HOI, a novel sparse temporal control framework for HOI video generation.
  arXiv  Detail & Related papers  (2026-07-07T08:29:25Z)
• From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation [54.68405211129937]
  Exo-Seqto-Ego video generation aims to synthesize a first-person view from a synchronized third-person and corresponding camera poses.<n> synchronized exo-ego data inherently introduces substantial-temporal discontinuities, violating the smooth-motion assumptions of standard video generation benchmarks.<n>We propose Syn2Ex-Forcing, a sequential sequence model that interpolates between the source and target videos to form a single continuous signal.
  arXiv  Detail & Related papers  (2026-04-15T12:32:25Z)
• Beyond Static Frames: Temporal Aggregate-and-Restore Vision Transformer for Human Pose Estimation [7.63512442757446]
  Vision Transformers (ViTs) have recently achieved state-of-the-art performance in 2D human pose estimation.<n> TAR-ViTPose enhances static ViT representations by aggregating temporal cues across frames in a plug-and-play manner.<n>Our approach outperforms existing state-of-the-art video-based methods, while also achieving a noticeably higher runtime frame rate in real-world applications.
  arXiv  Detail & Related papers  (2026-03-06T05:45:04Z)
• DCDM: Divide-and-Conquer Diffusion Models for Consistency-Preserving Video Generation [77.89090846233906]
  We propose a system-level framework, termed the Divide-and-Conquer Diffusion Model (DCDM)<n>DCDM decomposes video consistency modeling into three dedicated components while sharing a unified video generation backbone.<n>We validate our framework on the test set of the CVM Competition at AAAI'26, and the results demonstrate that the proposed strategies effectively address these challenges.
  arXiv  Detail & Related papers  (2026-02-14T07:02:36Z)
• MultiShotMaster: A Controllable Multi-Shot Video Generation Framework [67.38203939500157]
  Current generation techniques excel at single-shot clips but struggle to produce narrative multi-shot videos.<n>We propose MultiShotMaster, a framework for highly controllable multi-shot video generation.
  arXiv  Detail & Related papers  (2025-12-02T18:59:48Z)
• Arbitrary Generative Video Interpolation [27.953958715353608]
  Video frame (VFI) generates intermediate frames from given start and end frames.<n>Existing VFI methods are constrained to synthesize a fixed number of intermediate frames.<n>We present ArbInterp, a novel generative VFI framework that enables efficient synthesis at any timestamp.
  arXiv  Detail & Related papers  (2025-10-01T06:57:10Z)
• InfiniteTalk: Audio-driven Video Generation for Sparse-Frame Video Dubbing [66.48064661467781]
  We introduce sparse-frame video dubbing, a novel paradigm that strategically preserves references to maintain identity, iconic gestures, and camera trajectories.<n>We propose InfiniteTalk, a streaming audio-driven generator designed for infinite-length long sequence dubbing.<n> Comprehensive evaluations on HDTF, CelebV-HQ, and EMTD datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-08-19T17:55:23Z)
• VideoMolmo: Spatio-Temporal Grounding Meets Pointing [66.19964563104385]
  VideoMolmo is a model tailored for fine-grained pointing of video sequences.<n>A novel temporal mask fusion employs SAM2 for bidirectional point propagation.<n>To evaluate the generalization of VideoMolmo, we introduce VPoMolS-temporal, a challenging out-of-distribution benchmark spanning five real-world scenarios.
  arXiv  Detail & Related papers  (2025-06-05T17:59:29Z)
• Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition [43.84348967231349]
  Few-shot action recognition aims to recognize novel action categories with few exemplars.<n>Existing methods typically learn frame-level representations for each video by designing inter-frame temporal modeling strategies.<n>We propose HR2G-shot, a Hierarchical Relation-augmented Representation Generalization framework for FSAR.
  arXiv  Detail & Related papers  (2025-04-14T10:23:22Z)
• JointTuner: Appearance-Motion Adaptive Joint Training for Customized Video Generation [13.168628936598367]
  JointTuner is a framework that enables joint optimization of appearance and motion components.<n>AiT Loss disrupts the flow of appearance-related components, guiding the model to focus exclusively on motion learning.<n>JointTuner is compatible with both UNet-based models and Diffusion Transformer-based models.
  arXiv  Detail & Related papers  (2025-03-31T11:04:07Z)
• VrdONE: One-stage Video Visual Relation Detection [30.983521962897477]
  Video Visual Relation Detection (VidVRD) focuses on understanding how entities over time and space in videos. Traditional methods for VidVRD, challenged by its complexity, typically split the task into two parts: one for identifying what relation are present and another for determining their temporal boundaries. We propose VrdONE, a streamlined yet efficacious one-stage model for VidVRD.
  arXiv  Detail & Related papers  (2024-08-18T08:38:20Z)
• Exploring Motion and Appearance Information for Temporal Sentence Grounding [52.01687915910648]
  We propose a Motion-Appearance Reasoning Network (MARN) to solve temporal sentence grounding. We develop separate motion and appearance branches to learn motion-guided and appearance-guided object relations. Our proposed MARN significantly outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2022-01-03T02:44:18Z)
• Hierarchical Attention Network for Action Segmentation [45.19890687786009]
  The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. We propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time. We evaluate our system on challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets.
  arXiv  Detail & Related papers  (2020-05-07T02:39:18Z)
• Spatio-Temporal Ranked-Attention Networks for Video Captioning [34.05025890230047]
  We propose a model that combines spatial and temporal attention to videos in two different orders. We provide experiments on two benchmark datasets: MSVD and MSR-VTT. Our results demonstrate the synergy between the ST and TS modules, outperforming recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T01:00:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.