As briefly outlined on the home page, the Salient Poses algorithm first analyzes the motion capture animation to identity potential sets of keyframes that can be used to reconstruct the animation. As the user, you are expected to choose the particular set of keyframes best to your editing task. From there, the algorithm creates a simplified animation using contains only these keyframes. In the simplified animation, the inbetweens are tweaked to best recreate the original animation.

In this page, we dive deeper into the technical details of the algorithm. Next we will outline the steps used during execution, and then describe each one in detail. If you’re looking for more information, don’t forget to check out the paper.

[Image, Steps of the Algorithm]

The steps performed by of the Salient Poses are best described as analysis, selection, user choice, and reduction.

Analysis is the process of evaluating how well each pair of keyframes describes the motion. In selection, we use dynamic programming to compose an optimal set of keyframes for each level of compression. Next, the user chooses the particular set of keyframes best suited to their task. And, finally, reduction is the process of replacing non-keyframes with automatically generated in-betweens.


The first step is to evaluate how important each of the mocap’s keyframes are. In our current design, we do this by estimating how well every possible pair of keyframes approximates the motion between them. In particular, we form high-dimensional points from the poses of these keyframes and then interpolate them to form a high-dimensional line. We then compare this line against each of the poses of the frames between those keyframes, in which each pose forms another high-dimensional point. This formulation is useful, because it provides a geometric definition of what it means for keyframes to be important: a pair of keyframes is important when all poses between the keyframes are close to the interpolating line.

With the definition given above, this first analysis step can be performed by iterating through the pairs of keyframes and measuring the point-to-line distance described above.

Here’s the idea written down in Python code, in which we build an error table that records the error between the line spanning the keyframes and the frames between them. In this case error corresponds to the point-to-line distance and important is high when the error is low.

# Create a table of measuring error for each
# pair of keyframes.
def create_error_table():

    table = {}
    for pair in pairs_of_keyframes:

        # Calculate error as the largest point-to-line distance
        error = 0
        line = interpolate(pair)
        for frame in frames_between(pair):
            distance = point_to_line_distance(pair, frame)
            if distance > error:
                error = distance

        # Save the error to the table
        table[pair] = error

    return table

And that’s it for the first step. Note that there is no reason why we have to use this particular definition of importance. In fact, inspired by optimization methods, we intentionally designed Salient Poses to be able to use other definitions. We will explore other possible criteria for quantifying the usefulness of a keyframes in future work.


The second step performed by Salient Poses is selection, which is the process of choosing an optimal set of keyframes for each level of compression (a set of two keyframes, a set of three keyframes, a set of four, of five, and so on). Here the word optimal means that each keyframe is important or, in other words, that the set of keyframes minimize the total error from the table provided by the first analysis step.

Why is it hard?

A brute-force approach to find all optimal sets of keyframes - such as iterating through all possible combinations - is not possible. There are simply too many possible combinations for any computer, even a very fast one, to step through each of the alternatives. For what it’s wroth, the problem of finding an optimal set of keyframes is related to the infamous NP-hard Travelling Salesman Problem.

How can we solve it then?

Thankfully, dynamic programming comes to rescue. By adding the constraint that the problem has a beginning and an end - in our case this constraints means the first and last frames must be keyframes - we are able to express the problem to have something called an optimal substructure.

[Image, Dividing an Animation]

This idea of optimal substructure really just means that we can divide the problem into smaller pieces. To illustrate how this division works, imagine that we already have a simplified animation: an animation with just, say, nine optimal keyframes. Let’s say we cut the animation in half, with the split occurring at the fifth keyframe. Both halves now have five keyframes. If the original nine keyframes for the complete animation were as good as possible, then are the five keyframes for each half still as good as possible? It can be hard to see at first, but the answer is yes, the keyframes are optimal for both halves - nothing has changed about the underlying motion, there is no way that they cannot be optimal.

And now the solution!

Being sure that we can divide the problem into smaller pieces, we apply the dynamic programming to compose the optimal sets of keyframes. The key idea is to solve the smallest possible problem and then use the solution to help with solving the bigger problem.

Now for the hard part

Okay, so now we need to put together all the pieces. This is hardest part of the algorithm to understand. Try to read it over a couple of times, but don’t worry if you don’t get it right away - it took me years to come to terms with it. Feel free to join the Slack Community and ask me about directly if you’d like more information.

In practice, we solve the problem by starting off small (as small as possible) and grow the problem bigger until we’ve solved the problem.

As small as possible means we slice off just the first three keyframes of the animation. What’s the best choice of two keyframes for an animation containing just three keyframes? Because of the constraint we described above, we have to choose {1, 3}. And how about three keyframes? Well, there’s only one choice: {1, 2, 3}. Yes, this sounds crazy, just keep reading because it gets better. Moving up to an animation of four keyframes: the best choice of:

  • 2 keyframes is {1, 4},
  • 3 keyframes is {1, ?, 4}, and
  • 4 keyframes is {1, 2, 3, 4}.

Hey, what about that question mark? This is where the magic happens. We already know the answer. We simply look back and see whether the error (remember the analysis table) of {1, 2} or {1, 3} was lower. We choose the one with the least error, and plug that in as our solution of 3 keyframes for this animation of 4 frames.

Moving on to five keyframes, we’ve got:

  • 2 keyframes is {1, 5},
  • 3 keyframes is {1, ?, 5},
  • 4 keyframes is {1, ??, 5}, and
  • 5 keyframes is {1, 2, 3, 4, 5}.

For the problem of 3 keyframes, we compare {1, 2, 3} and {1, ?, 4}. Remember, we already solved the problem of {1, ?, 4}, so we can recall that from above. We then solve the problem of {1, ??, 5} by examining which of the smaller solutions has the least error: is it {1, 2, 3} or {1, ?, 4}?

What’s happening here is that we are substituting solutions from the small problems into the larger ones. This substitution means that we are able to save on computation by recycling previous computations.

Here’s another Python code example, which implements the selection step:

# Use substitution to compose optimal sets of keyframes
def select_keyframes(animation):

    # Do selection for increasing number of keyframes
    for _ in range(3, len(animation)):

        # Iterate over animations, from smallest to largest
        for (start, end) in animation.slices():

            # Iterate over the inbetween frames to find
            #  the best solution of `n` keyframes for this slice
            best_error = INFINITY
            best_selection = None

            for frame in (start + 1, end):
                smaller_selection = animation.get_best_selection_between(start, frame)
                additional_error = animation.get_error_between(frame, end)

                error_for_selection = max ( small_selection.error(), additional_error )
                if error_for_selection < best_error:
                    best_error = error_for_selection
                    best_selection = animation.create_selection(smaller_selection, frame)

In summary of the selection step, Salient Poses employs dynamic programming to substitute choices of keyframes from smaller slices of the animation into the choice for successively larger slices. By doing this, Salient Poses can make optimal choices without sacrificing performance (at least for the scope of problems we focus on).

User Choice

After the selection step has finished, all solutions - each optimal set of keyframes - are saved. It is up to the user to decide on the particular set that’s the best for them. There are many possible approaches to here, such as choosing a selection by choosing a number of keyframes or perhaps choosing the selection with the fewest keyframes that has an acceptable error. In our Maya implementation, the user can interactively browse the potential selections.

[Image, Choosing a Selection]


After the user has identified a particular set of keyframes, the last step is to perform the reduction. The idea of this step is to replace non-keyframes with inbetweens.

[Image, Curves Before And After]

Animation programs generate inbetweens by interpolating through keyframes using a special type of curve (you can see them in the image above). Each animated property - such as the bend of the elbow or the height of the foot - is assigned one curve. Each value along these curves corresponds to the data behind a keyframe. From this perspective, we can perform the reduction step by first removing keyed values from these curves and then tweaking the left-overs to best preserve the detail of our motion capture.

In practice, Salient Poses removes everything that’s not a keyframe. One that’s done, it uses the iterative fitting step from a famous Graphics Gem’s algorithm - its used in program’s like Adobe’s Illustator to smooth hand-drawn curves - to tweak the resulting curves so that the best represent the original animation.


And that’s it. In summary, Salient Poses converts hard-to-edit motion capture into easy-to-edit keyframe animation by first assigning an error value to each pair of keyframes, composing potential sets of keyframes using that error information, and then performs the reduction given a particular set of keyframes.

If you’re looking for more information, you might be interested to check out the paper. The results page contains a collection of before/after examples, and finally don’t forget to join the Slack Community if you’ve got questions!