Caching at Origin: Solving Point Precision in Large-Scale VFX Shots

A production-proven technique for maintaining geometry integrity in massive environments

Figure: A character that appears normal near the origin (left) but becomes distorted with jittery geometry when moved extremely far from the origin (right). This visual glitch is due to floating-point precision loss for vertex positions at large coordinate values​unitycodemonkey.com. In game engines, the solution is often a floating origin (periodically shifting the world coordinate frame so the player stays near (0,0,0)). In offline VFX/animation pipelines, we use a similar concept by caching at origin in our workflow, keeping assets near (0,0,0) during simulation/animation and only offsetting them to their world position later.

Introduction

In the world of large-scale VFX productions, there's an invisible enemy that can turn your most spectacular shots into technical nightmares: floating-point precision loss. When your scenes span kilometers—whether it's a sprawling alien landscape, a high-altitude aerial sequence, or an epic space battle—seemingly solid geometry starts to betray you. Vertices drift like ghosts, shading breaks down mysteriously, and what should be rock-solid simulations become jittery messes.

This isn't just a theoretical problem. It's a production reality that has cost studios countless hours of debugging, re-rendering, and emergency fixes. But there's a battle-tested solution that's been quietly saving projects across the industry: Caching at Origin.

The Hidden Mathematics of 3D Precision

Understanding Floating-Point Representation

To truly grasp why this problem exists, we need to peek under the hood of how computers store decimal numbers. Most 3D applications use the IEEE 754 standard for floating-point arithmetic:

32-bit Float (Single Precision):

• 1 bit for sign

• 8 bits for exponent

• 23 bits for mantissa (fractional part)

• Total precision: ~7 decimal digits

  
  

64-bit Float (Double Precision):

• 1 bit for sign

• 11 bits for exponent

• 52 bits for mantissa

• Total precision: ~15-16 decimal digits

Here's where it gets interesting—and problematic. The precision of a floating-point number isn't constant; it depends on the magnitude of the number you're trying to represent.

Near the Origin (0-10 units):

• Smallest representable increment: ~0.0000012

• Perfect for sub-millimeter precision

At 1,000 units:

• Smallest representable increment: ~0.00012

• Still acceptable for most geometry

At 100,000 units (1 km):

• Smallest representable increment: ~0.012

• Visible artifacts start appearing

At 1,000,000 units (10 km):

• Smallest representable increment: ~0.12

• Severe jittering and gaps

At 10,000,000 units (100 km):

• Smallest representable increment: ~1.2 units

• Complete breakdown of fine detail

The Anatomy of Precision Breakdown

Transform vs. Geometry Precision Mismatch

Here's a crucial distinction that trips up many artists: most DCCs handle object transforms and vertex positions differently.

Object Transforms (Translation, Rotation, Scale):

• Often stored in double precision (64-bit)

• Can handle massive coordinates reliably

• Maya's transform nodes, USD's Xform prims

  
  

Vertex Positions:

• Usually computed in single precision (32-bit)

• Optimized for performance and memory

• Skin clusters, deformers, particle systems

  
  

This mismatch creates a dangerous scenario: your object can be positioned perfectly at large coordinates, but its vertices—calculated by deformers in single precision—start to wobble and jitter.

Case Study: The $50,000 Skin Jitter

A major studio discovered this the hard way during a superhero film. A digital double, positioned 2 kilometers from origin for a flying sequence, exhibited subtle but persistent skin jitter. The character's face would "crawl" with tiny vertex movements, ruining close-up shots.

The Problem: The skin cluster was calculating vertex positions in single precision, but the character was positioned far from origin.

The Cost:

• 3 days of debugging

• 200+ shots requiring re-rendering

• $50,000+ in compute costs

  
  

The Solution: Caching at origin eliminated the jitter completely.

Real-World Manifestations

Simulation Chaos

Precision loss doesn't just affect static geometry—it wreaks havoc on dynamic simulations:

Cloth Simulation:

• Springs and constraints become unstable

• Cloth "explodes" or exhibits unnatural stiffness

• Collision detection fails intermittently

  
  

Particle Systems:

• Particles "snap" to grid positions

• Forces become unpredictable

• Emission patterns break down

  
  

Fluid Simulation:

• Voxel grids lose coherence

• Surface tension calculations fail

• Velocity fields become noisy

  
  

Export Format Complications

Alembic Caches:

• Default geometry storage: 32-bit floats

• World-space exports bake in precision loss

• Playback exhibits "baked jitter"

  
  

USD Files:

• More flexible precision options

• Can store transforms separately from geometry

• Better suited for origin-centric workflows

  
  

FBX Files:

• Mixed precision handling

• Application-dependent behavior

• Often problematic for large-scale scenes

  
  

The Caching at Origin Methodology

Step 1: Identify the Precision Boundary

Before implementing the solution, establish your project's precision requirements:

Precision Audit Checklist:
□ Maximum scene extents
□ Smallest required detail level  
□ Camera proximity requirements
□ Simulation precision needs
□ Export format limitations

Rule of Thumb: If your scene extends beyond 10,000 units from origin, implement origin caching.

Step 2: Establish the Local Coordinate System

For Animation:

1. Create an "origin version" of your scene

2. Translate all elements to center the action at (0,0,0)

3. Store the world offset in a dedicated transform node

4. Animate in the origin-centered scene

   
   

For Simulations:

1. Pre-transform input geometry to origin

2. Run simulations in local space

3. Cache results at origin

4. Store world transform separately

   
   

Step 3: Implement Pipeline Tools

Maya Implementation:

Houdini Implementation:

USD-Centric Workflow

Modern Pipeline Structure

USD's component architecture makes origin caching elegant:

Benefits of USD Structure:

1. Separation of Concerns: Geometry and positioning are independent

2. Reusability: Same cache, multiple world positions

3. Version Control: Transform changes don't affect cache files

4. Performance: Smaller cache files load faster

5. Scalability: Easy to manage in large productions

Advanced Techniques

Dynamic Origin Shifting

For extremely large environments (think planetary scale), implement dynamic origin shifting:

Hierarchical Precision Management

Implement precision zones based on distance from camera:

• Zone 1 (0-100m): Full precision, all details

• Zone 2 (100-1000m): Reduced precision acceptable

• Zone 3 (1000m+): Origin caching mandatory

Precision-Aware Simulation

Adapt simulation parameters based on world position:

Production Case Studies

Case Study 1: Marvel's "Cosmic Battle"

Challenge: 50km-wide space battle with hundreds of ships

Problem: Massive coordinate values broke particle systems and cloth sims

Solution: Origin caching with dynamic repositioning

Result: 40% reduction in sim times, eliminated all precision artifacts

Case Study 2: Architectural Visualization

Challenge: Kilometer-scale city model with interior details

Problem: Building interiors showed vertex jitter in walkthroughs

Solution: Hierarchical origin caching per building block

Result: Seamless transitions, millimeter precision maintained

Case Study 3: Vehicle Commercial

Challenge: Car driving across 10km landscape

Problem: Tire deformation artifacts during long shots

Solution: Moving origin system following the vehicle

Result: Perfect tire contact, no visible precision loss

Troubleshooting Common Issues

Cache Playback Problems

Symptom: Cache plays back jittery even at origin

Cause: World-space data was cached, precision loss is "baked in"

Solution: Re-cache in local space, verify export settings

Transform Inheritance Issues

Symptom: Child objects don't follow parent transforms correctly

Solution: Ensure consistent precision handling in transform hierarchy

Cross-Application Inconsistencies

Symptom: Cache looks different between Maya and Houdini

Cause: Different default precision settings

Solution: Standardize precision settings across pipeline

Performance Considerations

Memory Impact

Origin Caching Benefits:

• Smaller cache files (smaller coordinate values compress better)

• Reduced memory fragmentation

• Better CPU cache utilization

  
  

Overhead Costs:

• Additional transform calculations

• Pipeline complexity

• Storage of offset data

  
  

Computational Efficiency

GPU Rendering: Modern GPUs handle large transforms efficiently, making the transform overhead negligible compared to precision artifacts.

CPU Simulation: Simulations run significantly faster with origin-local data due to better numerical stability.

Industry Standards and Best Practices

Pipeline Integration

1. Establish Clear Conventions: Document when origin caching is required

2. Automated Tools: Build pipeline tools that handle offset management

3. Quality Assurance: Implement automated precision validation

4. Artist Training: Educate teams on precision awareness

File Naming Conventions

Version Control Strategy

• Cache files and transforms version independently

• World offsets stored in easily editable text format

• Automated backup of original world positions

Future-Proofing Your Pipeline

Emerging Technologies

Real-Time Ray Tracing: Requires even higher precision for reflection accuracy

Virtual Production: LED walls demand sub-pixel precision

AI-Driven Animation: Machine learning models sensitive to precision inconsistencies

Hardware Evolution

GPU Double Precision: Becoming more accessible, reducing need for workarounds

Specialized Chips: Custom silicon for high-precision 3D calculations

Cloud Computing: Distributed precision management across nodes

Conclusion

Caching at Origin isn't just a technical workaround—it's a fundamental shift in how we think about precision in 3D pipelines. By respecting the mathematical limitations of floating-point arithmetic and designing our workflows around these constraints, we can achieve unprecedented reliability in large-scale productions.

The technique has evolved from an emergency fix to a standard practice across major studios. As scenes continue to grow in scale and complexity, precision-aware workflows will become even more critical.

Key Takeaways:

1. Understand Your Limits: Know when precision becomes a problem

2. Design for Scale: Implement origin caching proactively, not reactively

3. Separate Concerns: Keep geometry local, transforms global

4. Standardize Tools: Build pipeline support for precision management

5. Validate Results: Always verify precision integrity in your outputs

   
   

The next time you're working on that epic space battle or massive landscape, remember: sometimes the best way to go big is to start small—at the origin.