Spatially-Rich Video Training Data

• Spatially-rich video training data is defined by explicit, dense spatial annotations that capture object geometry, context, and inter-frame relationships.
• Acquisition methods combine manual, automated, and simulation-based techniques to ensure temporally consistent and high-fidelity spatial labeling.
• Utilizing spatially-rich data enhances model performance, leading to improved recognition, segmentation, and multimodal spatial reasoning in video tasks.

Spatially-rich video training data refers to video datasets and sampling methodologies that maximize the explicit encoding, preservation, or annotation of spatial information—such as object locations, geometric context, boundary detail, and spatial relationships—within video sequences. Such data are fundamental for models that require not only temporal comprehension but also precise spatial awareness for recognition, segmentation, tracking, and downstream multimodal reasoning.

1. Principles of Spatially-Rich Video Data

Spatially-rich video data differs from conventional video datasets by prioritizing the preservation and explicit annotation of spatial features across frames. Critical attributes include:

• Object/location fidelity: Retaining or recovering the full geometric and region information of objects (e.g., pixelwise masks, keypoints, bounding boxes).
• Spatial context preservation: Maintaining intra-frame and inter-frame spatial structure, such as scene layout or relative object positioning.
• Dense spatial annotation: Annotating data at a fine spatial granularity, often at every pixel or segment, and providing information beyond simple object class tags.
• Temporal-spatial coupling: Ensuring that spatial information is trackable and consistent over time, supporting tasks like dense tracking, spatiotemporal reasoning, and object/action correspondence.

2. Acquisition and Curation Methods

Methodologies for curating spatially-rich video data span manual annotation, simulation, automated labeling, and self-supervised data shaping.

a) Manual and Semi-Automated Annotation

• Dense geometric labeling: "Geometric Context from Videos" presents a dataset of >20,000 frames each annotated at the segment level for spatial geometry (e.g., sky, ground, vertical classes) and vertical sub-classes, using hierarchical video segmentation and majority voting for spatial consistency (Raza et al., 2015).
• Bounding-box-to-boundary interpolation: Volumetric Graph Convolutional Networks (VGCN) generate dense region boundaries from sparse bounding boxes by modeling region control points in a spatio-temporal graph, propagating appearance, motion, and position features to all frames (Xu et al., 2020).
• Automated pipelines: Leader360V leverages a multi-stage label pipeline using patch-based equirectangular processing, entity/panoptic segmentors, and LLMs to deliver precise instance masks and tracking IDs for 360° video, accommodating highly non-Euclidean spatial domains (Zhang et al., 17 Jun 2025).

b) Synthetic and Simulation-Based Generation

• 3D simulator pipelines: SIMS-V systematically constructs spatially-annotated video data from 3D simulators, capturing perfect object geometry, egocentric positions, and scene structure; it then auto-generates richly varied spatial question–answer pairs for instruction-tuning spatial reasoning (Brown et al., 6 Nov 2025).
• Controlled motion curation: Boximator filers, tracks, and projects dynamic bounding boxes, using both hard (strict) and soft (region-based) spatial constraints to prepare object-centric, controllable video data for motion synthesis (Wang et al., 2 Feb 2024).

3. Annotation Types and Spatial Information Encoding

Approaches vary in the types and granularity of spatial data provided:

• Camera extrinsics/intrinsics and geometry: Datasets such as SpatialVID provide per-frame camera pose, dense depth, and dynamic masks, supporting full 3D scene grounding (Wang et al., 11 Sep 2025).
• Multi-modal spatial annotation: Motion2D-Video-150K features large-scale 2D human skeleton tracks for both individual and multi-person motion, annotated in lockstep with text descriptions emphasizing relational spatial context (Xi et al., 17 Jun 2025).
• Segmentation masks and edges: REVECA combines semantic segmentation masks (Mask2Former) with frame positional encoding to augment image representations with spatial subject information for event captioning (Heo et al., 2022). VidEdit uses panoptic segmentation masks and edge maps to confine text-driven generative edits to precise ROI boundaries in neural atlas space, ensuring fine spatial specificity (Couairon et al., 2023).
• Bounding box geometry and region attributes: StereoSync extracts bounding boxes and depth maps from video, encoding both the position/track of sound sources and global geometry to inform spatially-aware stereo audio generation (Marinoni et al., 7 Oct 2025).

4. Data Transformation and Sampling for Enhanced Spatial Representation

Several methods aim to increase the spatial richness of input data even when original datasets are limited:

• Gaussian-weighted frame aggregation: By aggregating $k$ consecutive frames into a super-frame via a Gaussian-weighted sum, more spatio-temporal information is preserved than by uniform or random frame sampling. This increases the density of motion and appearance cues available to 3D CNNs and downstream sequence models, resulting in measurable action recognition gains (Basha et al., 2020):

$$G(I_k, W) = \sum_{j=1}^{k} I_{k_j} \cdot \frac{W_j}{\sum_{j=1}^{k} W_j}$$

• 3D video transformation and sampling: V3S pre-processes videos along width, height, and time, introducing spatial scaling/projection (altering aspect and object geometry) and temporal resampling for direction/magnitude of motion. The model jointly predicts spatial and temporal transformation parameters, thereby forcing learned representations to encode spatial structure explicitly (Li et al., 2021).
• Augmented scene mixing and instruction synthesis: VISTA augments spatial richness by compositing multiple videos spatially (e.g., grid layouts), overlaying video "needles" into high-res "haystack" backgrounds, and generating location-aware question–answer pairs, producing synthetic long-duration or high-resolution video-instruction pairs (Ren et al., 1 Dec 2024).

5. Impact on Model Design and Downstream Performance

Spatially-rich video data is instrumental for advancing a wide range of video models and applications:

• Improved recognition and generalization: Enriching spatial signals in the training set (e.g., via aggregation or 3D sampling) directly translates to improved accuracy in human action recognition, video retrieval, and motion understanding, including in challenging low-occupancy scenarios (Basha et al., 2020, Li et al., 2021).
• Dense, temporally coherent representation learning: FRAME distills spatially precise patch features from strong image teachers (DINO, CLIP), then uses memory and anticipation modules to maintain both spatial and temporal fidelity, outperforming both traditional image and video encoders on dense prediction tasks such as object tracking and semantic segmentation (TV et al., 5 Jun 2025).
• Multitask 360° video learning: Large scale, semantically dense 360° video datasets with spatial annotation (Leader360V) enable foundation models to generalize better for both segmentation and tracking, demonstrating strong quantifiable improvements when training on such data (Zhang et al., 17 Jun 2025).
• Enabling spatial reasoning in multimodal LLMs: Simulated and real-world spatially-rich datasets equipped with detailed geometric and relational annotations empower video-LLMs to perform compositional, egocentric, and metric spatial reasoning that is otherwise unattainable with unannotated or coarse data (Brown et al., 6 Nov 2025, Qiu et al., 29 Nov 2024).

6. Synthetic Spatial Data and Real-World Transfer

Synthetic and generative approaches expand the availability of spatially-rich video data at scale:

• Diffusion-based video augmentation: Transforming static images into sequences via image-to-video diffusion models results in motion-plausible, semantically consistent video-flow pairs critical for two-stream saliency models, yielding superior results compared to traditional geometric warping (Cho et al., 21 Nov 2024).
• Simulation-grounded spatial instruction-tuning: Generating spatial video–question–answer triples with privileged ground truth (absolute distances, egocentric directions, object appearance order) in simulation achieves more effective transfer to real-world spatial reasoning benchmarks—more so than attempting to mirror the distribution of question types in evaluation data (Brown et al., 6 Nov 2025).
• Persistent knowledge and control: Boximator demonstrates that by isolating spatial control modules and using self-tracking for explicit box-object association, rich spatial control signals can be learned without impairing foundational generative model knowledge (Wang et al., 2 Feb 2024).

7. Representative Datasets and Comparative Table

The following table summarizes selected benchmark datasets supporting spatially-rich video learning:

Dataset	Clips/Frames/Hours	Spatial Annotation	Distinctive Features
Geometric Context	100 videos / 20k+ frames	Pixel/segment-level geometric context	Hierarchical labels, semi-supervised bootstrap
Leader360V	10,000+ clips	Per-frame instance, box, ID (198 classes)	Semantic harmonization, 360° FOV, LLM-driven pipeline
Motion2D-Video-150K	150,000 motion sequences	Multi-person 2D pose tracks (per-frame)	Balanced single/double character, textual description
SpatialVID	2.7M clips / 127M frames / 7k hr	Camera pose, depth, dynamic mask, captions	Serialized motion instructions, HQ subset
RoomTour3D	~100k traj / ~200k instr / 243h	3D trajectory, room type, bbox, depth	Real video, geometry and LLM-augmented navigation

• Gaussian-weighted aggregation for spatio-temporal preservation (Basha et al., 2020)
• 3D transformation and self-supervised learning (Li et al., 2021)
• Geometric context and semi-supervised spatial labeling (Raza et al., 2015)
• Dense boundary annotation from sparse boxes (Xu et al., 2020)
• FRAME, a spatially-precise temporally coherent encoder (TV et al., 5 Jun 2025)
• Leader360V, fully automated 360° spatial annotation (Zhang et al., 17 Jun 2025)
• VISTA spatiotemporal augmentation (Ren et al., 1 Dec 2024)
• Motion2D-Video-150K for interacting human skeletons (Xi et al., 17 Jun 2025)
• SIMS-V simulation-based instruction tuning (Brown et al., 6 Nov 2025)
• VidEdit panoptic-guided video region editing (Couairon et al., 2023)
• Diffusion-based generation from images for VSOD (Cho et al., 21 Nov 2024)
• Detailed dataset/process comparisons (Wang et al., 11 Sep 2025, Han et al., 11 Dec 2024)

Spatially-rich video training data is critical for progress in dense prediction, multimodal reasoning, controllable generation, and spatial intelligence and is realized through diverse, high-fidelity annotation, novel data augmentation, and simulation-based synthesis frameworks. These strategies collectively enhance spatial awareness in machine learning systems for video understanding and downstream vision-language applications.